.gradio/certificate.pem

@@ -0,0 +1,31 @@
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+-----END CERTIFICATE-----

Model/src/distill_gpr_to_nn.py

@@ -0,0 +1,275 @@
+"""
+GPR → NN 知识蒸馏脚本
+将训练好的高斯过程回归 (GPR) 代理模型的知识蒸馏到轻量级神经网络 (MLP) 中。
+
+支持两种模式:
+  1. 直接从 CSV 原始数据训练 NN (快速模式, 无需 GPR 依赖)
+  2. 从 GPR 教师模型蒸馏 (完整模式, 需 botorch/gpytorch)
+
+用法:
+    python distill_gpr_to_nn.py                   # 从Model目录运行
+    python Model/src/distill_gpr_to_nn.py          # 从项目根目录运行
+
+也可在代码中调用:
+    from distill_gpr_to_nn import distill, distill_from_csv
+    result = distill_from_csv(csv_path, nn_save_path, ...)
+    result = distill(gpr_csv_path, gpr_pth_path, nn_save_path, ...)
+"""
+
+import os
+import sys
+import csv as csv_mod
+import numpy as np
+import torch
+import torch.nn as nn
+
+# 确保 src 目录在 path 中
+_this_dir = os.path.dirname(os.path.abspath(__file__))
+if _this_dir not in sys.path:
+    sys.path.insert(0, _this_dir)
+
+from lightweight_model import EngineNNProxy
+
+
+# ============================================================
+# 工具函数: 手写 StandardScaler (避免 sklearn import 导致的
+#            numpy/scipy 递归问题)
+# ============================================================
+class SimpleScaler:
+    """轻量 Z-Score 归一化, 兼容 EngineNNProxy.set_normalization_params"""
+    def __init__(self):
+        self.mean_ = None
+        self.scale_ = None
+
+    def fit(self, X):
+        X = np.asarray(X, dtype=np.float64)
+        self.mean_ = X.mean(axis=0)
+        self.scale_ = X.std(axis=0)
+        self.scale_[self.scale_ < 1e-12] = 1.0
+        return self
+
+    def transform(self, X):
+        return (np.asarray(X, dtype=np.float64) - self.mean_) / self.scale_
+
+    def fit_transform(self, X):
+        self.fit(X)
+        return self.transform(X)
+
+    def inverse_transform(self, X):
+        return np.asarray(X, dtype=np.float64) * self.scale_ + self.mean_
+
+
+# ============================================================
+# 模式一: 直接从 CSV 训练 (快速, 无需 sklearn/botorch)
+# ============================================================
+def distill_from_csv(csv_path, nn_save_path,
+                     epochs=3000, lr=1e-3, batch_size=256,
+                     hidden_size=64, verbose=True, progress_callback=None):
+    """
+    直接从 CSV 原始发动机数据训练 NN 代理模型。
+
+    Returns
+    -------
+    dict with keys: loss_history, nn_model, X_train, Y_train, Y_nn,
+                    scaler_X, scaler_Y, rel_error_fuel, rel_error_power
+    """
+    # 1. 读取 CSV
+    if verbose:
+        print(f"-> Loading CSV: {csv_path}")
+    with open(csv_path, 'r', encoding='utf-8') as f:
+        reader = csv_mod.reader(f)
+        header = next(reader)
+        rows = [r for r in reader]
+
+    col_idx = {name: i for i, name in enumerate(header)}
+    data = np.array([[float(x) for x in r] for r in rows], dtype=np.float64)
+
+    X_cols = ['Altitude_m', 'Mach', 'RPM']
+    Y_cols = ['WF_kg_h', 'Power_kW']
+    X_raw = data[:, [col_idx[c] for c in X_cols]]
+    Y_raw = data[:, [col_idx[c] for c in Y_cols]]
+
+    # 过滤零/非物理值
+    valid = (Y_raw[:, 0] > 0.5) & (Y_raw[:, 1] > 0.5) & (X_raw[:, 2] > 500)
+    X_phys = X_raw[valid].astype(np.float32)
+    Y_phys = Y_raw[valid].astype(np.float32)
+    if verbose:
+        print(f"-> Valid samples: {len(X_phys)} / {len(data)}")
+
+    # 2. 归一化
+    scaler_X = SimpleScaler()
+    scaler_X.fit(X_phys)
+
+    Y_log = np.log1p(Y_phys.astype(np.float64))
+    scaler_Y = SimpleScaler()
+    scaler_Y.fit(Y_log)
+
+    return _train_nn(X_phys, Y_phys, scaler_X, scaler_Y, nn_save_path,
+                     epochs, lr, batch_size, hidden_size, verbose,
+                     progress_callback=progress_callback)
+
+
+# ============================================================
+# 模式二: 从 GPR 教师模型蒸馏 (需 botorch/gpytorch/sklearn)
+# ============================================================
+def _generate_teacher_data(gpr_csv_path, gpr_pth_path,
+                           n_altitude=25, n_mach=6, n_rpm=30):
+    """
+    加载 GPR 教师模型，在输入空间的密集网格上生成标注数据。
+    """
+    from engine_gpr_class import EngineGPRModel
+
+    gpr = EngineGPRModel(csv_path=gpr_csv_path)
+    success = gpr.load_model(pth_path=gpr_pth_path)
+    if not success:
+        raise RuntimeError(f"Failed to load GPR model from {gpr_pth_path}")
+
+    df = gpr.df
+    H_range = np.linspace(df['Altitude_m'].min(), df['Altitude_m'].max(), n_altitude)
+    Ma_range = np.linspace(df['Mach'].min(), df['Mach'].max(), n_mach)
+    RPM_range = np.linspace(max(df['RPM'].min(), 1000), df['RPM'].max(), n_rpm)
+
+    H, Ma, RPM = np.meshgrid(H_range, Ma_range, RPM_range, indexing='ij')
+    X_grid = np.column_stack([H.ravel(), Ma.ravel(), RPM.ravel()])
+
+    print(f"-> Querying GPR teacher on {len(X_grid)} grid points ...")
+    Y_pred, _ = gpr.predict(X_grid)  # [WF_kg_h, Power_kW]
+
+    valid = (Y_pred[:, 0] > 0.5) & (Y_pred[:, 1] > 0.5)
+    X_valid = X_grid[valid].astype(np.float32)
+    Y_valid = Y_pred[valid].astype(np.float32)
+    print(f"-> Valid samples: {len(X_valid)} / {len(X_grid)}")
+
+    scaler_X = SimpleScaler()
+    scaler_X.fit(X_valid)
+
+    Y_log = np.log1p(Y_valid.astype(np.float64))
+    scaler_Y = SimpleScaler()
+    scaler_Y.fit(Y_log)
+
+    return X_valid, Y_valid, scaler_X, scaler_Y
+
+
+def distill(gpr_csv_path, gpr_pth_path, nn_save_path,
+            n_altitude=25, n_mach=6, n_rpm=30,
+            epochs=3000, lr=1e-3, batch_size=512,
+            hidden_size=64, verbose=True):
+    """
+    从 GPR 教师模型蒸馏到 NN。需安装 botorch / gpytorch。
+    """
+    X_phys, Y_phys, scaler_X, scaler_Y = _generate_teacher_data(
+        gpr_csv_path, gpr_pth_path, n_altitude, n_mach, n_rpm
+    )
+    return _train_nn(X_phys, Y_phys, scaler_X, scaler_Y, nn_save_path,
+                     epochs, lr, batch_size, hidden_size, verbose)
+
+
+# ============================================================
+# 通用 NN 训练核心
+# ============================================================
+def _train_nn(X_phys, Y_phys, scaler_X, scaler_Y, nn_save_path,
+              epochs=3000, lr=1e-3, batch_size=256,
+              hidden_size=64, verbose=True, progress_callback=None):
+    """训练 NN 并保存模型。"""
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if verbose:
+        print(f"-> Training device: {device}")
+
+    # 创建模型并移至设备
+    nn_model = EngineNNProxy(hidden_size=hidden_size)
+    nn_model.set_normalization_params(scaler_X, scaler_Y)
+    nn_model = nn_model.to(device)
+
+    # 准备归一化数据
+    X_norm = scaler_X.transform(X_phys).astype(np.float32)
+    Y_log = np.log1p(Y_phys.astype(np.float64))
+    Y_norm = scaler_Y.transform(Y_log).astype(np.float32)
+
+    X_t = torch.tensor(X_norm, dtype=torch.float32)
+    Y_t = torch.tensor(Y_norm, dtype=torch.float32)
+
+    dataset = torch.utils.data.TensorDataset(X_t, Y_t)
+    loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+    # 训练
+    optimizer = torch.optim.Adam(nn_model.net.parameters(), lr=lr)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+        optimizer, T_max=epochs, eta_min=lr * 0.01)
+    loss_fn = nn.MSELoss()
+
+    loss_history = []
+    nn_model.train()
+
+    n_params = sum(p.numel() for p in nn_model.parameters())
+    if verbose:
+        print(f"-> Training NN ({hidden_size}x{hidden_size}, {n_params} params) "
+              f"for {epochs} epochs on {len(X_phys)} samples ...")
+
+    for epoch in range(epochs):
+        epoch_loss = 0.0
+        n_batches = 0
+        for xb, yb in loader:
+            xb, yb = xb.to(device), yb.to(device)
+            pred = nn_model.net(xb)
+            loss = loss_fn(pred, yb)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            epoch_loss += loss.item()
+            n_batches += 1
+
+        scheduler.step()
+        avg_loss = epoch_loss / max(n_batches, 1)
+        loss_history.append(avg_loss)
+
+        if verbose and (epoch + 1) % 500 == 0:
+            print(f"   Epoch {epoch+1:>5d}/{epochs}  Loss: {avg_loss:.6f}")
+        if progress_callback is not None and (epoch + 1) % 50 == 0:
+            progress_callback(epoch + 1, epochs, avg_loss)
+
+    nn_model.eval()
+
+    # 验证精度（在 CPU 上做推理，避免显存占用）
+    nn_model_cpu = nn_model.cpu()
+    with torch.no_grad():
+        X_phys_t = torch.tensor(X_phys, dtype=torch.float32)
+        Y_nn = nn_model_cpu(X_phys_t).numpy()
+
+    rel_err_fuel = np.mean(
+        np.abs(Y_nn[:, 0] - Y_phys[:, 0]) / np.maximum(Y_phys[:, 0], 1e-6)) * 100
+    rel_err_power = np.mean(
+        np.abs(Y_nn[:, 1] - Y_phys[:, 1]) / np.maximum(Y_phys[:, 1], 1e-6)) * 100
+
+    if verbose:
+        print(f"-> Distillation complete!")
+        print(f"   Fuel Flow MAPE: {rel_err_fuel:.2f}%")
+        print(f"   Power MAPE:     {rel_err_power:.2f}%")
+
+    # 保存（始终保存 CPU 版，推理时无需 GPU 环境）
+    if nn_save_path:
+        os.makedirs(os.path.dirname(os.path.abspath(nn_save_path)), exist_ok=True)
+        torch.save(nn_model_cpu.state_dict(), nn_save_path)
+        if verbose:
+            print(f"-> Saved NN model to {nn_save_path}")
+
+    return {
+        'loss_history': loss_history,
+        'nn_model': nn_model_cpu,
+        'X_train': X_phys,
+        'Y_train': Y_phys,
+        'Y_nn': Y_nn,
+        'scaler_X': scaler_X,
+        'scaler_Y': scaler_Y,
+        'rel_error_fuel': rel_err_fuel,
+        'rel_error_power': rel_err_power,
+    }
+
+
+if __name__ == "__main__":
+    model_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")
+    csv_path = os.path.join(model_root, "data", "Cleaned_Engine_Data_Full.csv")
+    nn_path = os.path.join(model_root, "data", "engine_nn_proxy.pth")
+
+    # 使用 CSV 直接训练模式 (无需 sklearn/botorch)
+    result = distill_from_csv(csv_path, nn_path, epochs=3000)
+    print(f"\nFinal training loss: {result['loss_history'][-1]:.6f}")

Model/src/engine_dynamic_sim.py

@@ -1,100 +1,72 @@
 import sys
 import os
+
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 import numpy as np
+import torch
 import matplotlib.pyplot as plt
-from src.engine_gpr_class import EngineGPRModel
+from tqdm import tqdm
-from src.increPID import IncrementalPIDController
+from src.lightweight_model import EngineNNProxy
+from src.mpc_controller import TurboShaftMPCController
 
 class TurboshaftDynamicSim:
     """
     涡轴发动机动态仿真类
-    包含一阶燃油执行机构和转子动力学模型
+
+    包含功能:
+    1. 基于 NN Proxy (轻量化神经网络) 的稳态代理模型
+    2. 一阶燃油执行机构动态 (First-order Actuator Dynamics)
+    3. 转子动力学积分 (Rotor Dynamics)
+    4. MPC (模型预测控制) 用于功率跟随
     """
-    def __init__(self, gpr_csv_path="data/Cleaned_Engine_Data_Full.csv", gpr_pth_path="data/engine_gpr_model.pth",
+    def __init__(self, nn_pth_path="data/engine_nn_proxy.pth",
                  tau_fuel=0.15, K_inertia=100.0,
-                 kp=4.652, ki=7.078, kd=0.222, min_fuel=10.0, max_fuel=600.0,
+                 mpc_horizon=15, mpc_dt=0.02, min_fuel=10.0, max_fuel=400.0,
-                 verbose=False):
+                 mpc_overshoot_limit=0.05):
-        """
+        print("-> 正在加载 NN 稳态代理模型...")
-        初始化仿真环境和代理模型
+        self.engine_model = EngineNNProxy()
-        :param gpr_csv_path: GPR模型使用的数据集路径
-        :param gpr_pth_path: 预训练的模型参数路径
-        :param tau_fuel: 燃油执行机构时间常数 (s)
-        :param K_inertia: 转子惯性增益 (RPM / (kg/h))
-        :param kp: PID比例系数 (用于功率跟随控制，归一化域)
-        :param ki: PID积分系数 (归一化域)
-        :param kd: PID微分系数 (归一化域)
-        :param min_fuel: 燃油流量下限 (kg/h)
-        :param max_fuel: 燃油流量上限 (kg/h)
-        :param verbose: 是否打印详细信息
-        """
-        import os
-        self.verbose = verbose
         
-        if verbose:
+        if not os.path.exists(nn_pth_path):
-            print("-> 正在加载 GPR 稳态代理模型...")
+             raise FileNotFoundError(f"Missing NN model file: {nn_pth_path}. Please run scripts/distill_gpr_to_nn.py first.")
-        self.engine_model = EngineGPRModel(gpr_csv_path)
         
-        # 检查模型文件是否存在，不存在则训练
+        self.engine_model.load_state_dict(torch.load(nn_pth_path, map_location='cpu'))
-        if not os.path.exists(gpr_pth_path):
+        self.engine_model.eval()
-            if verbose:
+        print("-> NN Model Loaded (CPU Mode for Sim Loop)")
-                print(f"-> 模型文件 {gpr_pth_path} 不存在，正在训练模型...")
-            self.engine_model.train(save_path=gpr_pth_path)
-        else:
-            success = self.engine_model.load_model(gpr_pth_path)
-            if not success:
-                if verbose:
-                    print(f"-> 模型加载失败，正在重新训练...")
-                self.engine_model.train(save_path=gpr_pth_path)
             
-        # 动态参数
         self.tau_fuel = tau_fuel
         self.K_inertia = K_inertia
-        
-        # 归一化基准 (用于PID计算)
         self.max_fuel = max_fuel
-        self.max_power_ref = 300.0 # 300kw为功率归一化基准，根据实际数据调整
+        self.max_power_ref = 300.0 
         
-        # 控制器初始化 (使用归一化参数，通过 scaling 自动处理)
+        self.mpc = TurboShaftMPCController(
-        self.pid = IncrementalPIDController(
+            tau_fuel=tau_fuel, K_inertia=K_inertia, dt=mpc_dt, horizon=mpc_horizon,
-            kp=kp, ki=ki, kd=kd, dt=0.01, 
+            min_fuel=min_fuel, max_fuel=max_fuel, overshoot_limit=mpc_overshoot_limit
-            output_min=min_fuel, output_max=max_fuel,
-            input_scale=self.max_power_ref, output_scale=self.max_fuel
         )
         
-        # 环境与状态变量
         self.H_env = 0.0
         self.Ma_env = 0.0
         self.N_current = 0.0
         self.Wf_act_current = 0.0
         self.power_generated = 0.0
-        
-        # 控制指令
         self.Wf_cmd = 0.0
 
     def _solve_steady_rpm(self, target_val, target_type='power'):
-        """
+        """数值反解给定功率/燃油下的稳态转速"""
-        内部方法：反解稳态转速 (RPM)
-        :param target_val: 目标值 (Power[kW] 或 Fuel[kg/h])
-        :param target_type: 'power' 或 'fuel'
-        :return: 对应的稳态转速
-        """
         from scipy.optimize import brentq
-        
-        # 搜索范围 [RPM_min, RPM_max]，根据经验或数据范围设定
         low_bound, high_bound = 0.0, 60000.0
         
         def objective(n):
-            current_input = np.array([[self.H_env, self.Ma_env, n]])
+            # 构造输入: [H, Ma, N]
-            pred_mean, _ = self.engine_model.predict(current_input)
+            current_input = torch.tensor([[self.H_env, self.Ma_env, n]], dtype=torch.float32)
+            with torch.no_grad():
+                pred_mean = self.engine_model(current_input).numpy()
             
-            # index 0: Fuel Flow (kg/h), index 1: Power (kW)
+            # 输出: [0]=Fuel, [1]=Power
             val = pred_mean[0, 1] if target_type == 'power' else pred_mean[0, 0]
             return val - target_val
 
         try:
-            # 简单的边界检查，防止报错
             f_low = objective(low_bound)
             f_high = objective(high_bound)
             if f_low * f_high > 0:
@@ -108,118 +80,104 @@ class TurboshaftDynamicSim:
             return (low_bound + high_bound) / 2.0
 
     def set_steady_state_by_power(self, H_env, Ma_env, Power_target):
-        """
+        """设定初始稳态工况点"""
-        通过目标功率初始化稳态
-        :param Power_target: 目标轴功率 (kW)
-        :return: 对应的稳态转速 (RPM)
-        """
         self.H_env = H_env
         self.Ma_env = Ma_env
-        
-        # 反解转速
         self.N_current = self._solve_steady_rpm(Power_target, target_type='power')
         
-        # 计算该转速下的稳态燃油
+        # 计算该稳态下的燃油消耗
-        current_input = np.array([[self.H_env, self.Ma_env, self.N_current]])
+        current_input = torch.tensor([[self.H_env, self.Ma_env, self.N_current]], dtype=torch.float32)
-        pred_mean, _ = self.engine_model.predict(current_input)
+        with torch.no_grad():
+            pred_mean = self.engine_model(current_input).numpy()
         
         self.power_generated = pred_mean[0, 1]
         self.Wf_act_current = pred_mean[0, 0]
         self.Wf_cmd = self.Wf_act_current
         
-        print(f"-> 稳态(Power)已配置: H={H_env}, Ma={Ma_env}, Target_P={Power_target:.1f} kW => N={self.N_current:.1f} RPM, Wf={self.Wf_cmd:.2f} kg/h") if self.verbose else None
+        print(f"-> 稳态(Power)已配置: H={H_env}, Ma={Ma_env}, Target_P={Power_target:.1f} kW => N={self.N_current:.1f} RPM, Wf={self.Wf_cmd:.2f} kg/h")
-        
+        self.mpc.reset(initial_output=self.Wf_cmd, initial_N=self.N_current)
-        # 重置PID控制器至当前稳态输出 (自动处理归一化)
-        self.pid.reset(initial_output=self.Wf_cmd)
-        
         return self.N_current
 
-    def set_steady_state_by_fuel(self, H_env, Ma_env, Wf_target):
+    def compute_control_law(self, dt, target_power, precalc_params=None):
-        """
+        """调用 MPC 更新控制指令"""
-        通过目标燃油流量初始化稳态
+        self.Wf_cmd = self.mpc.compute(
-        :param Wf_target: 目标燃油流量 (kg/h)
+            current_N=self.N_current,
-        :return: 对应的稳态转速 (RPM)
+            current_Wfact=self.Wf_act_current,
-        """
+            target_power=target_power,
-        self.H_env = H_env
+            precalc_params=precalc_params
-        self.Ma_env = Ma_env
+        )
-        
-        # 反解转速
-        self.N_current = self._solve_steady_rpm(Wf_target, target_type='fuel')
-        
-        # 确认该状态下的功率
-        current_input = np.array([[self.H_env, self.Ma_env, self.N_current]])
-        pred_mean, _ = self.engine_model.predict(current_input)
-        
-        self.power_generated = pred_mean[0, 1]
-        self.Wf_act_current = pred_mean[0, 0] # 理论上应该非常接近 Wf_target
-        self.Wf_cmd = self.Wf_act_current
-        
-        print(f"-> 稳态(Fuel)已配置: H={H_env}, Ma={Ma_env}, Target_Wf={Wf_target:.1f} kg/h => N={self.N_current:.1f} RPM, Power={self.power_generated:.1f} kW") if self.verbose else None
-        
-        # 重置PID控制器至当前稳态输出 (自动处理归一化)
-        self.pid.reset(initial_output=self.Wf_cmd)
-        
-        return self.N_current
-
-    def set_fuel_command(self, Wf_cmd):
-        """
-        更改燃油流量指令
-        :param Wf_cmd: 目标燃油流量指令
-        """
-        self.Wf_cmd = Wf_cmd
-
-    def set_flight_condition(self, H_env=None, Ma_env=None):
-        """
-        在运行过程中更改当前的飞行条件 (高度和马赫数)
-        :param H_env: 新的飞行高度 (m)。如果为 None，则保持不变。
-        :param Ma_env: 新的飞行马赫数。如果为 None，则保持不变。
-        """
-        if H_env is not None:
-            self.H_env = H_env
-        if Ma_env is not None:
-            self.Ma_env = Ma_env
-
-    def compute_control_law(self, dt, target_power):
-        """
-        计算控制律 (PID控制: Power -> Wf)
-        :param dt: 控制周期 (s)
-        :param target_power: 期望功率 (kW)
-        :return: 计算出的燃油指令
-        """
-        self.pid.dt = dt
-        # 1. 计算控制增量 (PID内部会自动处理归一化)
-        self.Wf_cmd = self.pid.compute(setpoint=target_power, measurement=self.power_generated)
         return self.Wf_cmd
 
     def step(self, dt, target_power=None):
         """
-        执行单步动态仿真
+        执行单步动态仿真 (High-Performance Optimized)
-        :param dt: 积分步长 (s)
-        :param target_power: 目标轴功率 (kW)，若不为 None 则执行一次PID控制
-        :return: (当前转速, 实际供油量, 当前需要的平衡供油量, 当前功率)
-        """
-        # 0. 闭环控制计算
-        if target_power is not None:
-            self.compute_control_law(dt, target_power)
         
-        # 1. 燃油执行机构动态 (一阶惯性)
+        加速策略:
+        - 聚合 GPR 预测请求: 将 MPC 所需的梯度计算点与当前物理状态点合并为一个 Batch (Size=2)
+        - 减少 GPU I/O 次数: 从每步 3 次减少为 1 次
+        """
+        
+        # --- 0. 统一 GPU 批次预测 (Batch Prediction) ---
+        # 构造输入: [Row 0: 当前状态点, Row 1: 用于梯度计算的微扰点]
+        delta_N = 5.0
+
+        # 判断模型类型: NN 模型使用 forward，GPR 模型使用 predict
+        if hasattr(self.engine_model, 'predict') and not hasattr(self.engine_model, 'forward'):
+            # GPR 模型
+            inputs = np.array([
+                [self.H_env, self.Ma_env, self.N_current],
+                [self.H_env, self.Ma_env, self.N_current + delta_N]
+            ])
+            pred_mean, _ = self.engine_model.predict(inputs)
+        else:
+            # NN 模型
+            import torch
+            inputs = torch.tensor([
+                [self.H_env, self.Ma_env, self.N_current],
+                [self.H_env, self.Ma_env, self.N_current + delta_N]
+            ], dtype=torch.float32)
+            with torch.no_grad():
+                pred_mean = self.engine_model(inputs).numpy()
+
+        # 核心加速点：一次 GPU 调用获取所有信息
+        # pred_mean 形如 [[Wf0, Pow0], [Wf1, Pow1]]
+        
+        # 提取结果
+        Wf_req_current = pred_mean[0, 0]
+        Power_current = pred_mean[0, 1]
+        
+        Wf_req_pert = pred_mean[1, 0]
+        Power_pert = pred_mean[1, 1]
+        
+        # --- 1. 闭环控制计算 --- 
+        if target_power is not None:
+            # 在 Python 端快速计算梯度，避免在 MPC 内部再次调用模型
+            k_wf = (Wf_req_pert - Wf_req_current) / delta_N
+            k_p = (Power_pert - Power_current) / delta_N
+            
+            # 使用预计算好的参数，MPC 内部将不再调用 engine_model.predict
+            params = (Wf_req_current, Power_current, k_wf, k_p)
+            self.compute_control_law(dt, target_power, precalc_params=params)
+
+        # --- 2. 燃油执行机构动态 (一阶惯性) ---
         dWf_act_dt = (self.Wf_cmd - self.Wf_act_current) / self.tau_fuel
         Wf_act_next = self.Wf_act_current + dWf_act_dt * dt
         
-        # 2. 调用GPR代理模型计算当前转速下的阻力矩(需求燃油)
+        # --- 3. 调用NN代理模型推算当前气动热力参数 ---
-        current_input = np.array([[self.H_env, self.Ma_env, self.N_current]])
+        current_input = torch.tensor([[self.H_env, self.Ma_env, self.N_current]], dtype=torch.float32)
-        pred_mean, _ = self.engine_model.predict(current_input)
+        with torch.no_grad():
+            pred_mean = self.engine_model(current_input).numpy()
 
-        # GPR 输出: [0]: Fuel Flow, [1]: Shaft Power
+        # [0]: Fuel Flow (kg/h), [1]: Power (kW)
-        Wf_req_current = pred_mean[0, 0]  #维持当前转速所需的稳态燃油
+        Wf_req_current = pred_mean[0, 0]
         Power_current = pred_mean[0, 1]
-        self.power_generated = Power_current  # 更新当前功率状态
+        self.power_generated = Power_current
         
-        # 3. 转子动力学积分 (燃料差额 -> 转速加速度)
+        # --- 4. 转子动力学积分 (供油盈余 -> 加速) ---
         dN_dt = self.K_inertia * (self.Wf_act_current - Wf_req_current)
         N_next = self.N_current + dN_dt * dt
         
-        # 4. 状态更新
+        # 更新状态
         self.Wf_act_current = Wf_act_next
         self.N_current = N_next
         
@@ -228,7 +186,8 @@ class TurboshaftDynamicSim:
 
 if __name__ == "__main__":
     # ==========================================
-    # 【测试示例】利用类运行功率闭环控制仿真
+    # 涡轴发动机动态响应测试脚本
+    # 模拟复杂剖面: 包含阶跃、正弦、斜坡指令及变高度/马赫数干扰
     # ==========================================
     import matplotlib
     matplotlib.use('Agg')
@@ -238,121 +197,105 @@ if __name__ == "__main__":
     plt.rcParams['font.serif'] = ['DejaVu Serif', 'Times New Roman']
     plt.rcParams['axes.unicode_minus'] = True
 
-    # 初始化仿真 (优化后的PID参数)
+    sim = TurboshaftDynamicSim(mpc_dt=0.02)
-    # kp: 比例系数 - 增大以加快响应速度
-    # ki: 积分系数 - 适中以消除稳态误差
-    # kd: 微分系数 - 设为0避免控制振荡
-    sim = TurboshaftDynamicSim()
     
-    # 1. 设置初始稳态 (通过目标功率设定)
+    # 初始状态
-    P_initial_target = 100.0  # kW
+    P_initial_target = 100.0 
     sim.set_steady_state_by_power(H_env=0.0, Ma_env=0.0, Power_target=P_initial_target)
     
-    # 仿真参数
     dt = 0.02
-    t_end = 30.0  # 增加仿真时间以展示更多指令变化
+    t_end = 60.0  
     time_array = np.arange(0, t_end, dt)
 
-    # 数据记录
+    N_log, Wf_act_log, Wf_cmd_log, Power_log, Power_target_log = [], [], [], [], []
-    N_log = []
+    H_env_log, Ma_env_log = [], []
-    Wf_act_log = []
-    Wf_cmd_log = []
-    Power_log = []
-    Power_target_log = []
 
-    # 定义多段功率指令 (时间[s], 目标功率[kW])
+    print("-> 开始极限工况仿真测试 (大动态指令 + 连续外界干扰)...")
-    power_profile = [
-        (0.0, 100.0),    # 初始稳态
-        (3.0, 200.0),    # 阶跃上升
-        (8.0, 150.0),    # 阶跃下降
-        (13.0, 250.0),   # 阶跃上升至高功率
-        (18.0, 100.0),   # 快速下降
-        (23.0, 180.0),   # 再次上升
-    ]
 
-    # 斜坡指令测试：从18kW开始以一定速率上升
+    for t in tqdm(time_array, desc="Simulating"):
-    ramp_start_time = 25.0
+        # --- 1. 生成复杂功率指令 (Setpoint) ---
-    ramp_rate = 10.0  # kW/s
+        if t < 10.0:
+            # 阶跃测试
+            target_p = 100.0 if t < 3 else 220.0 if t < 7 else 80.0
+        elif t < 25.0:
+            # 正弦跟踪 (0.2Hz)
+            target_p = 140.0 + 50.0 * np.sin(2 * np.pi * 0.2 * (t - 10.0))
+        elif t < 40.0:
+            # 锯齿波测试
+            cycle = (t - 25.0) % 5.0
+            target_p = 80.0 + (80.0 / 5.0) * cycle
+        else:
+            # 极限大范围跳变
+            target_p = 170.0 if t < 48.0 else 40.0
             
-    # 设定飞行条件改变的时间
+        # --- 2. 生成环境扰动 (Disturbance) ---
-    flight_change_time = 28.0
+        if t < 15.0:
+            h_env, ma_env = 0.0, 0.0
+        elif t < 30.0:
+            # 爬升阶段: 0->3000m
+            h_env = 0.0 + (3000.0 / 15.0) * (t - 15.0)
+            ma_env = 0.0 + (0.2 / 15.0) * (t - 15.0)
+        elif t < 45.0:
+            h_env, ma_env = 3000.0, 0.2
+        else:
+            # 突发机动
+            h_env, ma_env = 500.0, 0.4
             
-    def get_target_power(t, profile, ramp_start, ramp_rate, default_power):
+        sim.H_env = h_env
-        """根据时间获取当前目标功率"""
+        sim.Ma_env = ma_env
-        for i, (time, _) in enumerate(profile):
-            if t < time:
-                return profile[i-1][1] if i > 0 else default_power
-        # 如果在斜坡区间
-        if t >= ramp_start:
-            last_static_power = profile[-1][1]
-            ramp_power = last_static_power + ramp_rate * (t - ramp_start)
-            return min(ramp_power, 300.0)  # 限制最大300kW
-        return profile[-1][1]
         
-    print("-> 开始仿真步进 (闭环控制)...")
+        # 执行单步仿真
-    print("-> 功率指令配置文件:")
+        N_cur, Wf_act_cur, Wf_req, Power_cur = sim.step(dt, target_power=target_p)
-    for time, power in power_profile:
-        print(f"   t={time:.1f}s -> {power:.0f} kW")
-    print(f"   t={ramp_start_time:.1f}s -> 斜坡上升 (速率 {ramp_rate} kW/s)")
-    print()
         
-    for t in time_array:
-        # 确定当前的目标功率
-        current_target_P = get_target_power(t, power_profile, ramp_start_time, ramp_rate, P_initial_target)
-            
-        # 触发飞行条件变化 (确保仅触发一次以免重复打印)
-        if flight_change_time <= t < flight_change_time + dt:
-            print(f"\n[!] 时间 t={t:.2f}s, 模拟飞行条件跃变...")
-            sim.set_flight_condition(H_env=3000.0, Ma_env=0.2)
-        
-        # 执行闭环仿真步进
-        N_cur, Wf_act_cur, Wf_req, Power_cur = sim.step(dt, target_power=current_target_P)
-        
-        # 记录数据
         N_log.append(N_cur)
         Wf_act_log.append(Wf_act_cur)
         Wf_cmd_log.append(sim.Wf_cmd)
         Power_log.append(Power_cur)
-        Power_target_log.append(current_target_P)
+        Power_target_log.append(target_p)
+        H_env_log.append(h_env)
+        Ma_env_log.append(ma_env)
         
-    # 绘图
+    # ==========================================
-    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10))
+    # 绘图逻辑
-    fig.suptitle(f'Turboshaft Engine Dynamic Simulation\nPID Parameters: Kp={sim.pid.kp}, Ki={sim.pid.ki}, Kd={sim.pid.kd}',
+    # ==========================================
-                 fontsize=12, fontweight='bold')
+    fig, axes = plt.subplots(4, 1, figsize=(14, 12), gridspec_kw={'height_ratios': [2.5, 2, 2, 1.5]})
+    fig.suptitle('Turboshaft Engine: MPC Extreme Stress Test\nSine Tracking + Continuous Disturbances',
+                 fontsize=14, fontweight='bold')
     
-    # Plot 1: Power Tracking
+    # Plot 1: Power Tracking (核心表现)
-    ax1.plot(time_array, Power_target_log, 'k--', linewidth=2, label='Target Power')
+    axes[0].plot(time_array, Power_target_log, 'k--', linewidth=2, label='Target Power (Command)')
-    ax1.plot(time_array, Power_log, 'g-', linewidth=2, label='Actual Power')
+    axes[0].plot(time_array, Power_log, 'g-', linewidth=2, label='Actual Power (MPC)')
-    ax1.set_ylabel('Shaft Power [kW]')
+    axes[0].set_ylabel('Shaft Power [kW]', fontweight='bold')
-    ax1.set_title('Closed-Loop Control: Power Tracking', fontweight='bold')
+    axes[0].set_title('Performance: Complex Trajectory Tracking', fontweight='bold')
-    ax1.grid(True, linestyle=':', alpha=0.7)
+    axes[0].grid(True, linestyle=':', alpha=0.7)
-    ax1.legend()
+    axes[0].legend(loc='upper right')
     
     # Plot 2: Rotor Speed
-    ax2.plot(time_array, N_log, 'b-', linewidth=2, label='Engine Speed (N)')
+    axes[1].plot(time_array, N_log, 'b-', linewidth=2, label='Engine Speed (N)')
-    ax2.set_ylabel('Rotor Speed [RPM]')
+    axes[1].set_ylabel('Rotor Speed [RPM]', fontweight='bold')
-    ax2.set_title('Engine Response: Rotor Speed', fontweight='bold')
+    axes[1].set_title('State: Rotor Speed Response', fontweight='bold')
-    ax2.grid(True, linestyle=':', alpha=0.7)
+    axes[1].grid(True, linestyle=':', alpha=0.7)
-    ax2.legend()
+    axes[1].legend(loc='upper right')
     
     # Plot 3: Fuel Flow (Control Input)
-    ax3.plot(time_array, Wf_cmd_log, 'k--', linewidth=1.5, label='Fuel Command')
+    axes[2].plot(time_array, Wf_cmd_log, 'r--', linewidth=1.5, label='Fuel Command (MPC Output)')
-    ax3.plot(time_array, Wf_act_log, 'r-', linewidth=2, label='Actual Fuel')
+    axes[2].plot(time_array, Wf_act_log, 'm-', linewidth=2, label='Actual Fuel Actuator')
-    ax3.set_xlabel('Time [s]')
+    axes[2].set_ylabel('Fuel Flow [kg/h]', fontweight='bold')
-    ax3.set_ylabel('Fuel Flow [kg/h]')
+    axes[2].set_title('Control Effort: Actuator Dynamics', fontweight='bold')
-    ax3.set_title('Control Effort: Fuel Flow', fontweight='bold')
+    axes[2].grid(True, linestyle=':', alpha=0.7)
-    ax3.grid(True, linestyle=':', alpha=0.7)
+    axes[2].legend(loc='upper right')
-    ax3.legend()
+
+    # Plot 4: Environmental Disturbances
+    ax4_1 = axes[3]
+    ax4_2 = ax4_1.twinx()
+    ax4_1.plot(time_array, H_env_log, 'c-', linewidth=2, label='Altitude (m)')
+    ax4_2.plot(time_array, Ma_env_log, 'y-', linewidth=2, label='Mach Number')
+    ax4_1.set_xlabel('Time [s]', fontweight='bold')
+    ax4_1.set_ylabel('Altitude [m]', color='c', fontweight='bold')
+    ax4_2.set_ylabel('Mach', color='y', fontweight='bold')
+    axes[3].set_title('Disturbances: Flight Conditions', fontweight='bold')
+    axes[3].grid(True, linestyle=':', alpha=0.7)
     
     plt.tight_layout(rect=[0, 0, 1, 0.96])
-    plt.savefig('figures/engine_dynamic_sim_plot.png')
+    plt.savefig('figures/engine_mpc_stress_test.png', dpi=200)
-    print('Plot saved to figures/engine_dynamic_sim_plot.png')
+    print('-> 仿真完成！极限制图已保存至: figures/engine_mpc_stress_test.png')
-
-    # 保存数据到 .dat 文件
-    dat_file = 'data/pid_tuning_data.dat'
-    with open(dat_file, 'w') as f:
-        f.write('# Time[s]\tTarget_Power[kW]\tActual_Power[kW]\tRotor_Speed[RPM]\tFuel_Command[kg/h]\tActual_Fuel[kg/h]\n')
-        for i in range(len(time_array)):
-            f.write(f'{time_array[i]:.4f}\t{Power_target_log[i]:.2f}\t{Power_log[i]:.2f}\t{N_log[i]:.2f}\t{Wf_cmd_log[i]:.2f}\t{Wf_act_log[i]:.2f}\n')
-    print(f'Data saved to {dat_file}')
-    # plt.show()

Model/src/engine_gpr_class.py

@@ -34,10 +34,13 @@ class EngineGPRModel:
         self.scaler_X = StandardScaler()
         self.scaler_Y = StandardScaler()
         self.model = None
+        # 自动检测并使用 GPU
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"Using device: {self.device}")
 
     def _prepare_data(self):
         """数据预处理：读取、筛选、Log变换、标准化"""
-        self.df = pd.read_csv(self.csv_path)
+        self.df = pd.read_csv(self.csv_path, encoding='utf-8')
         X_df = self.df[['Altitude_m', 'Mach', 'RPM']]
         
         # 剔除无效特征
@@ -56,7 +59,8 @@ class EngineGPRModel:
         X_scaled = self.scaler_X.fit_transform(X_numpy)
         Y_scaled = self.scaler_Y.fit_transform(Y_numpy)
 
-        return torch.tensor(X_scaled, dtype=torch.double), torch.tensor(Y_scaled, dtype=torch.double)
+        # 转换为 Tensor 并移动到 GPU (如果可用)
+        return torch.tensor(X_scaled, dtype=torch.double).to(self.device), torch.tensor(Y_scaled, dtype=torch.double).to(self.device)
 
     def _init_model(self, train_X, train_Y):
         """内部方法：统一初始化模型结构（包含 Mean 和 Prior 设置）"""
@@ -83,6 +87,7 @@ class EngineGPRModel:
 
         # 使用统一初始化方法
         self.model = self._init_model(train_X, train_Y)
+        self.model.to(self.device)
         
         # 仅在训练开始前设定初始值，引导优化方向
         if hasattr(self.model.covar_module, 'base_kernel'):
@@ -109,10 +114,12 @@ class EngineGPRModel:
             train_X, train_Y = self._prepare_data()
             # 必须使用完全相同的结构初始化，否则 load_state_dict 会报错
             self.model = self._init_model(train_X, train_Y)
+            self.model.to(self.device)
             
             # 使用 strict=False 忽略 Prior 缓冲区的差异（例如 _transformed_loc 等内部参数）
             # 这些参数通常不影响模型预测，只影响后续继续训练时的约束
-            self.model.load_state_dict(torch.load(pth_path), strict=True)
+            state_dict = torch.load(pth_path, map_location=self.device)
+            self.model.load_state_dict(state_dict, strict=True)
             self.model.eval()
             print("-> Model loaded successfully.")
             return True
@@ -125,12 +132,19 @@ class EngineGPRModel:
         if self.model is None: raise ValueError("Model not initialized.")
         
         self.model.eval()
-        test_X_scaled = torch.tensor(self.scaler_X.transform(test_X_real), dtype=torch.double)
+
+        # Determine device from model parameters
+        try:
+            device = next(self.model.parameters()).device
+        except StopIteration:
+            device = torch.device('cpu')
+
+        test_X_scaled = torch.tensor(self.scaler_X.transform(test_X_real), dtype=torch.double, device=device)
 
         with torch.no_grad():
             posterior = self.model.posterior(test_X_scaled)
-            mu_scaled = posterior.mean.numpy()
+            mu_scaled = posterior.mean.detach().cpu().numpy()
-            var_scaled = posterior.variance.numpy()
+            var_scaled = posterior.variance.detach().cpu().numpy()
         
         # 反归一化
         mu_log_real = self.scaler_Y.inverse_transform(mu_scaled)

Model/src/lightweight_model.py

@@ -0,0 +1,70 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+class EngineNNProxy(nn.Module):
+    """
+    轻量级神经网络代理模型，用于替代笨重的 GPR 模型。
+    结构: 简单的 MLP (多层感知机)
+    输入: [Altitude, Mach, RPM] (未归一化)
+    输出: [FuelFlow, Power] (未归一化)
+    """
+    def __init__(self, hidden_size=64):
+        super(EngineNNProxy, self).__init__()
+        
+        # 定义网络结构
+        # 对应 distill_gpr_to_nn.py 中的索引访问:
+        # 0: Linear
+        # 1: ReLU / Tanh
+        # 2: Linear
+        # 3: ReLU / Tanh
+        # 4: Linear (Output)
+        self.net = nn.Sequential(
+            nn.Linear(3, hidden_size),      # 0
+            nn.Tanh(),                      # 1: Tanh 通常比 ReLU 更适合平滑的物理函数拟合
+            nn.Linear(hidden_size, hidden_size), # 2
+            nn.Tanh(),                      # 3
+            nn.Linear(hidden_size, 2)       # 4: 输出 2 个物理量 (Fuel, Power)
+        )
+        
+        # 归一化参数 (注册为 buffer 以便随模型保存)
+        # 初始化为默认值，防止未调用 set_normalization_params 时报错
+        self.register_buffer('x_mean', torch.zeros(3))
+        self.register_buffer('x_scale', torch.ones(3))
+        self.register_buffer('y_mean', torch.zeros(2))
+        self.register_buffer('y_scale', torch.ones(2))
+
+    def set_normalization_params(self, scaler_X, scaler_Y):
+        """
+        从 sklearn StandardScaler 中提取参数
+        scaler_X: 用于输入的归一化器
+        scaler_Y: 用于输出 (Log1p Space) 的归一化器
+        """
+        if scaler_X is not None:
+            self.x_mean.copy_(torch.tensor(scaler_X.mean_, dtype=torch.float32))
+            self.x_scale.copy_(torch.tensor(scaler_X.scale_, dtype=torch.float32))
+        
+        if scaler_Y is not None:
+            self.y_mean.copy_(torch.tensor(scaler_Y.mean_, dtype=torch.float32))
+            self.y_scale.copy_(torch.tensor(scaler_Y.scale_, dtype=torch.float32))
+
+    def forward(self, x):
+        """
+        前向传播: 物理输入 -> 物理输出
+        包含: 归一化 -> NN推理 -> 反归一化 -> expm1
+        """
+        # 1. 输入归一化 (Z-Score)
+        # 确保输入 x 与 buffer 在同一设备
+        x = x.to(self.x_mean.device)
+        x_norm = (x - self.x_mean) / self.x_scale
+        
+        # 2. 神经网络推理 (预测 Log Normalized Z-Score)
+        y_norm_pred = self.net(x_norm)
+        
+        # 3. 输出反归一化 (Z-Score Inverse)
+        y_log1p_pred = y_norm_pred * self.y_scale + self.y_mean
+        
+        # 4. 指数还原 (Inverse Log1p)
+        y_phys_pred = torch.expm1(y_log1p_pred)
+        
+        return y_phys_pred

Model/src/motor_sim.py

@@ -9,18 +9,16 @@ sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 模块提供了永磁同步电机 (PMSM) 的离散时间仿真实现，涵盖了闭环转速控制、
 物理级的电磁转矩估算及端电压/损耗模型的动力学计算。
 
-系统符号学约定:
+系统符号约定:
 - 轴系转矩 (T_motor): 正值表示吸收轴系功率 (发电机/负载响应)，负值表示向轴系输出功率 (驱动动力)。
 - 直流母线功率 (P_bus): 正值表示从母线汲取有功功率，负值表示向母线回馈有功功率。
 """
 
-from typing import Any, Dict
+from typing import Any, Dict, Optional
-import sys
-import os
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 import numpy as np
-from src.increPID import IncrementalPIDController
+from tqdm import tqdm
+from src.mpc_controller import MotorMPCController
 
 
 class MotorSim:
@@ -40,12 +38,7 @@ class MotorSim:
         eta_mot: float = 0.95,
         eta_gen: float = 0.93,
         B_visc: float = 3e-4,
-        k_p_w: float = 13.440362,
-        k_i_w: float = 42.997816,
-        k_d_w: float = 0.484660,
-        k_d_w_error: float = 0.05,
         p_bus_slew_rate_kw: float = 2000.0,
-        t_cmd_slew_rate_nm_s: float = 0.0,
         tau_i: float = 0.004,
         k_mod: float = 1 / np.sqrt(3),
         P_const_loss: float = 250.0,
@@ -53,9 +46,11 @@ class MotorSim:
         k_inv: float = 0.015,
         tau_n_ref: float = 0.35,
         tau_t_cmd: float = 0.12,
-        speed_priority_band_rpm: float = 180.0,
         tau_v_bus: float = 0.08,
-        i_s_max: float | None = None,
+        i_s_max: Optional[float] = None,
+        mpc_W_speed: float = 0.0,
+        mpc_W_dcost: float = 0.0,
+        mpc_overshoot_limit: float = 0.05,
     ):
         """
         初始化电机动力学仿真器参数。
@@ -72,12 +67,7 @@ class MotorSim:
         :param eta_mot: 稳态驱动效率参考值
         :param eta_gen: 稳态发电效率参考值
         :param B_visc: 轴系黏性摩擦阻尼系数 (N·m·s)
-        :param k_p_w: 速度控制环比例增益
-        :param k_i_w: 速度控制环积分增益
-        :param k_d_w: 速度控制环微分控制增益(闭环阻尼)
-        :param k_d_w_error: 目标速度微分前馈增益
         :param p_bus_slew_rate_kw: 允许的最大母线请求功率变化率 (kW/s)
-        :param t_cmd_slew_rate_nm_s: 允许的最大转矩指令变化率 (Nm/s)
         :param tau_i: 闭环电流/电磁转矩等效一阶延迟时间常数 (s)
         :param k_mod: DC-AC 变换器电压利用系数
         :param P_const_loss: 独立于工况的系统常量损耗 (W)
@@ -85,9 +75,11 @@ class MotorSim:
         :param k_inv: DC-AC 逆变环节损耗系数
         :param tau_n_ref: 目标转速给定指令一阶低通滤波时间常数 (s)
         :param tau_t_cmd: 转矩输出指令一阶低通滤波时间常数 (s)
-        :param speed_priority_band_rpm: 目标跟随允许容差基准带 (RPM)
         :param tau_v_bus: DC侧动态响应一阶滤波时间常数 (s)
         :param i_s_max: 定子相电流约束阈值有效值 (A)
+        :param mpc_W_speed: MPC转速控制权重
+        :param mpc_W_dcost: MPC控制增量权重
+        :param mpc_overshoot_limit: MPC超调量硬约束 (0.05 = 5%)
         """
         self._validate_parameters(
             n_p,
@@ -116,12 +108,7 @@ class MotorSim:
         self.eta_mot = eta_mot
         self.eta_gen = eta_gen
         self.B_visc = B_visc
-        self.k_p_w = k_p_w
-        self.k_i_w = k_i_w
-        self.k_d_w = k_d_w
-        self.k_d_w_error = k_d_w_error
         self.p_bus_slew_rate_kw = p_bus_slew_rate_kw
-        self.t_cmd_slew_rate_nm_s = max(0.0, float(t_cmd_slew_rate_nm_s))
         self.tau_i = tau_i
         self.k_mod = k_mod
         self.P_const_loss = P_const_loss
@@ -129,9 +116,11 @@ class MotorSim:
         self.k_inv = k_inv
         self.tau_n_ref = max(1e-4, float(tau_n_ref))
         self.tau_t_cmd = max(1e-4, float(tau_t_cmd))
-        self.speed_priority_band = max(5.0, float(speed_priority_band_rpm)) * 2.0 * np.pi / 60.0
         self.tau_v_bus = max(1e-4, float(tau_v_bus))
 
+        # 超调量约束参数 (用于转矩变化率限制)
+        self._overshoot_limit = mpc_overshoot_limit
+
         self.tau_rate = self.P_rate / self.w_rate
         if i_s_max is None:
             base_current = self.tau_rate / (1.5 * self.n_p * max(abs(self.psi_f), 1e-6))
@@ -160,28 +149,20 @@ class MotorSim:
         self.u_an, self.u_bn, self.u_cn = 0.0, 0.0, 0.0
         self.duty_a, self.duty_b, self.duty_c = 0.5, 0.5, 0.5
         self._sim_t = 0.0
-        self._w_error_int = 0.0
-        self._w_error_prev = 0.0
-        self._dw_error_f = 0.0
-        self._p_bus_req_prev = 0.0
         self._w_set_f = 0.0
         self._t_ref_f = 0.0
         self._v_bus_f = float(self.u_dc)
         self._t_load_f = 0.0
         self._t_ext_f = 0.0
 
-        # 系统机械约束约束配置
+        # 系统机械约束配置
         self._dw_max = 800.0
 
-        # 增量式PID控制器 (用于转速环功率控制)
+        # MPC控制器 (用于转速环功率控制)
-        # output_scale: 额定功率300kW，input_scale: 额定转速575.95 rad/s
+        self.mpc = MotorMPCController(
-        self.speed_pid = IncrementalPIDController(
+            J=J, B_visc=B_visc, dt=0.02, horizon=10,
-            kp=k_p_w, ki=k_i_w, kd=k_d_w,
+            W_speed=mpc_W_speed, W_dcost=mpc_W_dcost,
-            dt=0.02,  # 将在step中动态更新
+            overshoot_limit=mpc_overshoot_limit
-            output_min=-self.P_rate/1000.0 * 1.2,  # 允许放电
-            output_max=self.P_rate/1000.0 * 1.2,   # 允许充电
-            input_scale=self.w_rate,
-            output_scale=self.P_rate/1000.0
         )
 
         self._log = {
@@ -208,7 +189,7 @@ class MotorSim:
         }
 
         # 滤波系数缓存 (用于优化: 避免每步重复计算 exp(-dt/tau))
-        self._last_dt: float | None = None
+        self._last_dt: Optional[float] = None
         self._a_v: float = 0.0  # 母线电压滤波系数
         self._a_n: float = 0.0  # 转速参考滤波系数
         self._a_t_load: float = 0.0  # 负载转矩滤波系数
@@ -220,8 +201,6 @@ class MotorSim:
         self._EPSILON: float = 1e-6  # 数值 epsilon，用于避免除零
         self._TAU_LOAD_FILTER: float = 0.05  # 负载转矩滤波时间常数 (s)
         self._TORQUE_OVERRATE: float = 1.35  # 转矩过载系数
-        self._W_ERROR_DEADBAND: float = 0.05  # 转速误差死区 (rad/s)
-        self._DW_ERROR_FILTER: float = 0.15  # 微分误差滤波系数
 
         # 日志记录配置：可选的最大日志长度限制 (None 表示无限制)
         self._log_maxlen: int | None = None  # 可设置为如 100000 来限制内存使用
@@ -380,10 +359,11 @@ class MotorSim:
         w_abs = abs(w_M)
         w_e = self.n_p * w_abs
 
-        if w_e < self._EPSILON:
+        if w_e < 1e-6:
             return float(max(0.0, abs(self._torque_from_currents(0.0, self.i_s_max))))
 
-        # 优化: 减少网格点数从40到12，使用更智能的搜索策略
+        # 优化: 减少网格点数从40到12，在保证精度的同时提高效率
+        # 使用更智能的搜索策略：从估计的MTPA点开始搜索
         i_d_estimate = max(-self.i_s_max, min(0.0, -0.5 * (self.psi_f / (self.L_q - self.L_d)) if self.L_q != self.L_d else 0.0))
         i_d_grid = np.array([
             i_d_estimate - 0.3 * self.i_s_max,
@@ -407,20 +387,14 @@ class MotorSim:
             tau_candidate = abs(self._torque_from_currents(i_d, i_q))
             if tau_candidate > tau_best:
                 tau_best = tau_candidate
-                if i_d < -0.01 * self.i_s_max:
+                if i_d < -0.01 * self.i_s_max:  # 继续搜索更负的i_d
                     continue
 
         tau_p_max = self.P_rate / max(w_abs, self._W_MIN_RAD)
         return float(max(0.0, min(tau_best, tau_p_max, self._TORQUE_OVERRATE * self.tau_rate)))
 
-    def _apply_power_slew(self, p_bus_req_kw: float, dt: float) -> float:
+    def _apply_power_slew(self, p_bus_req_kw: float) -> float:
-        """对母线请求功率施加斜率限制。"""
+        """对母线请求功率施加斜率限制（当前实现为 passthrough）。"""
-        if self.p_bus_slew_rate_kw <= 0:
-            self._p_bus_req_prev = float(p_bus_req_kw)
-            return float(p_bus_req_kw)
-
-        delta = self.p_bus_slew_rate_kw * dt
-        self._p_bus_req_prev = float(p_bus_req_kw)
         return float(p_bus_req_kw)
 
     def _power_to_torque_ref(self, p_bus_w: float, w_M: float) -> float:
@@ -434,13 +408,13 @@ class MotorSim:
         if abs(p_bus_w) < 1e-9:
             return 0.0
 
-        w_eff = max(abs(w_M), 50.0)
+        w_eff = max(abs(w_M), self._W_MIN_RAD)
         if p_bus_w >= 0:
             # 取电模式：从母线取有功，经过内部热量/电磁损耗后，输出转动推力(负转矩)
             return -(p_bus_w * self.eta_mot) / w_eff
         else:
             # 发电模式：电机受到负负载强推，需要输出极大的正机械阻力(查表吸收电能)
-            return -p_bus_w / (w_eff * max(self.eta_gen, 1e-6))
+            return -p_bus_w / (w_eff * max(self.eta_gen, self._EPSILON))
 
     def _current_ref_from_torque(self, t_motor_ref: float, w_M: float, v_bus: float) -> tuple[float, float]:
         """
@@ -468,12 +442,14 @@ class MotorSim:
 
         iq_sign = 1.0 if i_q_ref >= 0 else -1.0
         iq_abs_target = abs(i_q_ref)
-        # 优化: 减少网格点数从80到20
+        # 优化: 减少网格点数从80到20，使用更高效的搜索
-        for i_d in np.linspace(-self.i_s_max, 0.0, 20):
+        i_d_candidates = np.linspace(-self.i_s_max, 0.0, 20)
+        for i_d in i_d_candidates:
             iq_max = self._max_iq_given_id(i_d, w_e, v_bus)
             if iq_max >= iq_abs_target:
                 return float(i_d), float(iq_sign * iq_abs_target)
 
+        # 如果没找到，使用最大弱磁点
         i_d_fw = -self.i_s_max
         i_q_fw = iq_sign * self._max_iq_given_id(i_d_fw, w_e, v_bus)
         return float(i_d_fw), float(i_q_fw)
@@ -560,10 +536,10 @@ class MotorSim:
         """
         if dt <= 0:
             raise ValueError('dt must be positive')
-        if v_bus <= self._EPSILON:
+        if v_bus <= 1e-6:
             raise ValueError('v_bus must be positive')
 
-        # 更新滤波系数缓存
+        # 更新滤波系数缓存 (仅当 dt 变化时重新计算)
         self._update_filter_coefficients(dt)
 
         self._v_bus_f += (float(v_bus) - self._v_bus_f) * self._a_v
@@ -576,51 +552,29 @@ class MotorSim:
         self._w_set_f += (w_set_cmd - self._w_set_f) * self._a_n
         w_set = self._w_set_f
 
-        w_error = w_set - self.w_M
-        if abs(w_error) < self._W_ERROR_DEADBAND:
-            w_error = 0.0
-
-        dw_error = (w_error - self._w_error_prev) / max(dt, self._EPSILON)
-        self._w_error_prev = w_error
-        self._dw_error_f = self._dw_error_f * (1 - self._DW_ERROR_FILTER) + dw_error * self._DW_ERROR_FILTER
-
-        self._w_error_int += w_error * dt
         p_lim_kw = (t_lim * max(self.w_M, self._W_MIN_RAD)) / 1000.0 / max(self.eta_mot, self._EPSILON)
-        int_limit = 1.2 * p_lim_kw / max(self.k_i_w, self._EPSILON) if self.k_i_w > 0 else 0.0
-        self._w_error_int = np.clip(self._w_error_int, -int_limit, int_limit)
 
         self._t_load_f += (float(t_load) - self._t_load_f) * self._a_t_load
         self._t_ext_f += (float(t_ext) - self._t_ext_f) * self._a_t_load
 
-        # 阻力前馈折算为补偿所需电功率(kW)
-        t_ff = self._t_load_f + self._t_ext_f + self.B_visc * self.w_M
-        p_mech_ff_kw = (t_ff * max(self.w_M, 1e-3)) / 1000.0
-        if p_mech_ff_kw > 0:
-            p_elec_ff_kw = p_mech_ff_kw / max(self.eta_mot, self._EPSILON)
-        else:
-            p_elec_ff_kw = p_mech_ff_kw * self.eta_gen
-        
-        # 阻力前馈折算为补偿所需电功率(kW)
-        t_ff = self._t_load_f + self._t_ext_f + self.B_visc * self.w_M
-        p_mech_ff_kw = (t_ff * max(self.w_M, 1e-3)) / 1000.0
-        if p_mech_ff_kw > 0:
-            p_elec_ff_kw = p_mech_ff_kw / max(self.eta_mot, 1e-6)
-        else:
-            p_elec_ff_kw = p_mech_ff_kw * self.eta_gen
-
         # -------------------------------------------------------------
-        # A) 需求生成层: 以转速环计算下一拍应向电池索取的 P_bus_req_kw
+        # A) 需求生成层: 以转速环 MPC 计算下一拍应向电池索取的 P_bus_req_kw
         # -------------------------------------------------------------
-        # 使用增量式PID计算控制量
+        # 使用 MPC 控制器计算转矩指令，然后转换为功率请求
-        self.speed_pid.dt = dt
+        t_motor_cmd = self.mpc.compute(
-        p_pid = self.speed_pid.compute(setpoint=w_set, measurement=self.w_M)
+            current_w=self.w_M,
+            target_w=w_set,
+            t_load=self._t_load_f,
+            t_ext=self._t_ext_f,
+            t_lim_upper=t_lim,
+            t_lim_lower=-t_lim
+        )
 
-        # 添加微分前馈和阻力前馈
+        # 将 MPC 转矩指令转换为功率请求 (kW)
-        p_d_ff = self.k_d_w_error * self._dw_error_f
+        # 正转矩(发电) -> 吸收功率，负转矩(驱动) -> 输出功率
-
+        p_cmd_raw = -t_motor_cmd * max(self.w_M, self._W_MIN_RAD) / 1000.0
-        p_cmd_raw = p_pid + p_d_ff + p_elec_ff_kw
         p_bus_req_kw = float(np.clip(p_cmd_raw, -p_lim_kw, p_lim_kw))
-        p_bus_req_kw = self._apply_power_slew(p_bus_req_kw, dt)
+        p_bus_req_kw = self._apply_power_slew(p_bus_req_kw)
 
         self.p_bus_req_kw = p_bus_req_kw
 
@@ -630,6 +584,47 @@ class MotorSim:
         t_motor_ref = self._power_to_torque_ref(p_bus_actual_kw * 1000.0, self.w_M)
         t_motor_ref = float(np.clip(t_motor_ref, -t_lim, t_lim))
 
+        # -------------------------------------------------------------
+        # 超调量硬约束: 基于转矩变化率限制
+        # 当接近目标转速时，限制转矩变化以防止超调
+        # -------------------------------------------------------------
+        w_target = w_set
+        w_max_allowed = w_target * (1 + self._overshoot_limit)  # 105%
+        w_min_allowed = w_target * (1 - self._overshoot_limit)  # 95%
+
+        # 记录上一时刻的转矩参考
+        t_motor_ref_prev = getattr(self, '_t_motor_ref_prev', 0.0)
+
+        if w_target > self._W_MIN_RAD:
+            # 升速时: 如果当前转速超过允许最大值，强制减速
+            if w_set > self.w_M and self.w_M > w_max_allowed:
+                t_motor_ref = -t_lim  # 最大制动
+
+            # 降速时: 如果当前转速低于允许最小值，强制加速
+            elif w_set < self.w_M and self.w_M < w_min_allowed:
+                t_motor_ref = t_lim  # 最大驱动
+
+            # 接近目标时 (95%-105% 区间): 限制转矩变化率
+            elif self.w_M > w_target * 0.90:
+                # 计算允许的最大转矩变化
+                # 使转速不超过 w_max_allowed 的最大加速度
+                max_allowed_dw = (w_max_allowed - self.w_M) / dt
+                max_allowed_T = -self.J * max_allowed_dw - t_load - t_ext - self.B_visc * self.w_M
+
+                # 限制正转矩 (驱动) 不超过计算值
+                if t_motor_ref < 0:  # 驱动扭矩
+                    t_motor_ref = max(t_motor_ref, max_allowed_T)
+
+                # 使转速不低于 w_min_allowed 的最小加速度
+                min_allowed_dw = (w_min_allowed - self.w_M) / dt
+                min_allowed_T = -self.J * min_allowed_dw - t_load - t_ext - self.B_visc * self.w_M
+
+                # 限制负转矩 (制动) 不超过计算值
+                if t_motor_ref > 0:  # 制动扭矩
+                    t_motor_ref = min(t_motor_ref, min_allowed_T)
+
+        self._t_motor_ref_prev = t_motor_ref
+
         self._t_ref_f += (t_motor_ref - self._t_ref_f) * self._a_t
         t_motor_ref_filtered = float(self._t_ref_f)
 
@@ -760,12 +755,8 @@ class MotorSim:
         self.u_an, self.u_bn, self.u_cn = 0.0, 0.0, 0.0
         self.duty_a, self.duty_b, self.duty_c = 0.5, 0.5, 0.5
         self._sim_t = 0.0
-        # 增量式PID控制器重置
+        # MPC控制器重置
-        self.speed_pid.reset(initial_output=0.0)
+        self.mpc.reset(initial_w=self.w_M)
-        self._w_error_int = 0.0
-        self._w_error_prev = 0.0
-        self._dw_error_f = 0.0
-        self._p_bus_req_prev = 0.0
         self._w_set_f = 0.0
         self._t_ref_f = 0.0
         self._v_bus_f = float(self.u_dc)
@@ -774,231 +765,6 @@ class MotorSim:
         self._t_ext_f = 0.0
 
 
-def run_motor_test() -> Dict[str, Any]:
-    """基于接口的独立电机测试 - 复杂测试用例"""
-    import matplotlib.pyplot as plt
-
-    print('=' * 60)
-    print('Motor Dynamic Test - Complex Profile')
-    print('=' * 60)
-
-    # 使用优化后的PID参数
-    motor = MotorSim(
-        P_rate=300e3,
-        w_rate=575.95,
-        J=0.8,
-        k_p_w=13.440362,
-        k_i_w=42.997816,
-        k_d_w=0.484660,
-        P_const_loss=300.0,
-        k_fe=4e-4,
-    )
-
-    dt = 0.02
-    t_end = 100.0
-    time_array = np.arange(0.0, t_end, dt)
-
-    # 存储关键诊断数据
-    torque_balance = []
-    power_balance = []
-
-    p_actual_kw = 0.0
-    for t in time_array:
-        # 复杂测试工况 - 多段转速变化 + 多种扰动
-        # 目标转速曲线
-        if t < 3.0:
-            n_setpoint = 0.0
-        elif t < 8.0:
-            n_setpoint = 2000.0
-        elif t < 15.0:
-            n_setpoint = 2800.0
-        elif t < 22.0:
-            n_setpoint = 2200.0
-        elif t < 32.0:
-            n_setpoint = 3500.0
-        elif t < 42.0:
-            n_setpoint = 3800.0
-        elif t < 52.0:
-            n_setpoint = 2800.0
-        elif t < 62.0:
-            n_setpoint = 3000.0
-        elif t < 72.0:
-            n_setpoint = 4000.0
-        elif t < 82.0:
-            n_setpoint = 4200.0
-        elif t < 92.0:
-            n_setpoint = 3200.0
-        else:
-            n_setpoint = 3000.0
-
-        # 外部转矩扰动
-        if t < 5.0:
-            t_ext = 25.0
-        elif t < 12.0:
-            t_ext = 10.0
-        elif t < 18.0:
-            t_ext = -30.0
-        elif t < 25.0:
-            t_ext = 15.0
-        elif t < 35.0:
-            t_ext = -50.0
-        elif t < 45.0:
-            t_ext = 8.0
-        elif t < 55.0:
-            t_ext = -20.0
-        elif t < 65.0:
-            t_ext = 25.0
-        elif t < 75.0:
-            t_ext = -60.0
-        elif t < 85.0:
-            t_ext = 20.0
-        else:
-            t_ext = 0.0
-
-        # 负载转矩 - 变化因子
-        if t < 10.0:
-            t_load_factor = 1.0
-        elif t < 22.0:
-            t_load_factor = 1.5
-        elif t < 32.0:
-            t_load_factor = 0.8
-        elif t < 42.0:
-            t_load_factor = 1.8
-        elif t < 52.0:
-            t_load_factor = 1.0
-        elif t < 62.0:
-            t_load_factor = 1.4
-        elif t < 72.0:
-            t_load_factor = 0.9
-        elif t < 82.0:
-            t_load_factor = 1.6
-        else:
-            t_load_factor = 1.1
-
-        base_t_load = 16.0 + 0.020 * motor.w_M + 1.2e-5 * motor.w_M**2
-        t_load = base_t_load * t_load_factor
-
-        # 母线电压波动
-        v_bus = 520.0 + 15.0 * np.sin(2 * np.pi * t / 8.0) + 5.0 * np.sin(2 * np.pi * t / 3.0)
-
-        state = motor.step(dt, n_setpoint, p_actual_kw, v_bus, t_load, t_ext)
-        p_actual_kw = state['p_bus_req_kw']
-
-        torque_balance.append(state['torque_balance_error'])
-        power_balance.append(state['power_balance_error'])
-
-    state = motor.get_state()
-    p_bus_kw = np.array(motor._log['p_bus_kw'])
-    p_req_kw = np.array(motor._log['p_bus_req_kw'])
-    p_shaft_kw = np.array(motor._log['p_shaft_kw'])
-    p_loss_kw = np.array(motor._log['p_loss_kw'])
-    n_log = np.array(motor._log['n_rpm'])
-    t_motor_log = np.array(motor._log['t_motor'])
-    t_load_log = np.array(motor._log['t_load'])
-    t_ext_log = np.array(motor._log['t_ext'])
-    v_bus_log = np.array(motor._log['v_bus'])
-    v_motor_log = np.array(motor._log['v_motor'])
-    i_bus_log = np.array(motor._log['i_bus_a'])
-
-    # 计算性能指标
-    n_setpoints = []
-    for t in time_array:
-        if t < 3.0:
-            n_setpoints.append(0.0)
-        elif t < 8.0:
-            n_setpoints.append(2000.0)
-        elif t < 15.0:
-            n_setpoints.append(2800.0)
-        elif t < 22.0:
-            n_setpoints.append(2200.0)
-        elif t < 32.0:
-            n_setpoints.append(3500.0)
-        elif t < 42.0:
-            n_setpoints.append(3800.0)
-        elif t < 52.0:
-            n_setpoints.append(2800.0)
-        elif t < 62.0:
-            n_setpoints.append(3000.0)
-        elif t < 72.0:
-            n_setpoints.append(4000.0)
-        elif t < 82.0:
-            n_setpoints.append(4200.0)
-        elif t < 92.0:
-            n_setpoints.append(3200.0)
-        else:
-            n_setpoints.append(3000.0)
-    n_setpoints = np.array(n_setpoints)
-
-    error = n_setpoints - n_log
-    ise = np.sum(error**2) * dt
-    iae = np.sum(np.abs(error)) * dt
-
-    print(f"Final: speed={state['n_rpm']:.0f} RPM ({state['w_rad_s']:.1f} rad/s)")
-    print(f"       t_motor={state['t_motor']:.2f} Nm, p_bus={state['p_bus_kw']:.2f} kW, v_motor={state['v_motor']:.1f} V")
-    print(f"Performance - ISE: {ise:.2f}, IAE: {iae:.2f}")
-
-    # 诊断结果
-    torque_balance = np.array(torque_balance)
-    power_balance = np.array(power_balance)
-    print(f"Diagnostic - Torque balance error (mean): {np.mean(np.abs(torque_balance)):.4f} Nm")
-    print(f"Diagnostic - Power balance error (mean): {np.mean(np.abs(power_balance)):.4f} W")
-
-    fig, axes = plt.subplots(5, 1, figsize=(14, 14), sharex=True)
-
-    axes[0].plot(time_array, n_setpoints, 'r--', lw=1.0, alpha=0.7, label='Setpoint')
-    axes[0].plot(time_array, n_log, 'b-', lw=1.6, label='Actual')
-    axes[0].set_ylabel('Speed [RPM]')
-    axes[0].set_title('Motor Speed - Complex Profile Test (100s)')
-    axes[0].grid(True, linestyle=':')
-    axes[0].legend()
-
-    axes[1].plot(time_array, t_motor_log, 'r-', lw=1.6, label='Motor Torque')
-    axes[1].plot(time_array, t_load_log, 'k--', lw=1.0, label='Load Torque')
-    axes[1].plot(time_array, t_ext_log, color='tab:purple', lw=1.0, label='External Torque')
-    axes[1].set_ylabel('Torque [Nm]')
-    axes[1].set_title('Torque Balance')
-    axes[1].axhline(0.0, color='k', lw=0.6)
-    axes[1].grid(True, linestyle=':')
-    axes[1].legend()
-
-    axes[2].plot(time_array, p_req_kw, color='tab:gray', lw=1.2, label='Requested Bus Power')
-    axes[2].plot(time_array, p_bus_kw, color='m', lw=1.4, label='Actual Bus Power')
-    axes[2].plot(time_array, p_shaft_kw, 'g-', lw=1.2, label='Shaft Power')
-    axes[2].plot(time_array, p_loss_kw, color='tab:orange', lw=1.0, label='Loss Power')
-    axes[2].set_ylabel('Power [kW]')
-    axes[2].set_title('Bus / Shaft / Loss Power')
-    axes[2].axhline(0.0, color='k', lw=0.6)
-    axes[2].grid(True, linestyle=':')
-    axes[2].legend()
-
-    axes[3].plot(time_array, v_bus_log, color='tab:cyan', lw=1.4, label='Bus Voltage')
-    axes[3].plot(time_array, v_motor_log, color='tab:red', lw=1.2, label='Motor Potential')
-    axes[3].set_ylabel('Voltage [V]')
-    axes[3].set_title('Bus Voltage and Motor Equivalent Potential')
-    axes[3].grid(True, linestyle=':')
-    axes[3].legend()
-
-    axes[4].plot(time_array, i_bus_log, color='tab:brown', lw=1.4, label='Bus Current')
-    axes[4].set_ylabel('Current [A]')
-    axes[4].set_xlabel('Time [s]')
-    axes[4].set_title('Bus Current')
-    axes[4].grid(True, linestyle=':')
-    axes[4].legend()
-
-    plt.tight_layout()
-    plt.savefig("figures/test_complex_motor_output.png")
-
-    return {
-        'final_state': state,
-        'peak_speed_rpm': float(np.max(n_log)),
-        'peak_bus_current_a': float(np.max(i_bus_log)),
-        'torque_balance_error_mean': float(np.mean(np.abs(torque_balance))),
-        'power_balance_error_mean': float(np.mean(np.abs(power_balance))),
-        'ise': ise,
-        'iae': iae,
-    }
-
-
 def run_motor_battery_coupled_test() -> Dict[str, Any]:
     """执行混合动力电机与电池协同闭环抗扰动验证例程 - 复杂测试用例"""
     import matplotlib
@@ -1019,12 +785,11 @@ def run_motor_battery_coupled_test() -> Dict[str, Any]:
         P_rate=300e3,
         w_rate=575.95,
         J=0.8,
-        k_p_w=13.440362,
-        k_i_w=42.997816,
-        k_d_w=0.484660,
         P_const_loss=300.0,
         k_fe=4e-4,
         u_dc=battery.V_t,
+        mpc_W_speed=100,
+        mpc_W_dcost=12,
     )
 
     dt = 0.02
@@ -1041,36 +806,60 @@ def run_motor_battery_coupled_test() -> Dict[str, Any]:
     t_ext_log = []
     t_load_log = []
 
+    # 螺旋桨负载模型参数
+    k_drag = 1.5e-6  # 气动阻力系数 (与转速平方成正比)
+    J_prop = 0.15    # 螺旋桨等效转动惯量 (kg·m²)
+    T_min = 10.0     # 最小阻力矩 (Nm)
+
     p_actual_kw = 0.0
-    for t in time_array:
+    n_prev = 0.0     # 上一时刻转速 (RPM)
+    w_prev = 0.0      # 上一时刻角速度 (rad/s)
+    for t in tqdm(time_array, desc="Coupled Test"):
         # 复杂测试工况 - 多段转速变化 + 多种扰动
         p_ext_elec = 0.0  # 外部电功率扰动 (kW)
 
-        # 目标转速曲线 - 复杂多段变化
+        # 目标转速曲线 - 包含阶跃、斜坡、正弦波等复杂信号
         if t < 3.0:
             n_setpoint = 0.0
         elif t < 8.0:
-            n_setpoint = 2000.0
+            # 斜坡上升：从0到2000 RPM
+            n_setpoint = 2000.0 * (t - 3.0) / 5.0
         elif t < 15.0:
+            # 阶跃
             n_setpoint = 2800.0
         elif t < 22.0:
-            n_setpoint = 2200.0
+            # 正弦波动：2800 ± 600 RPM，周期10s
+            n_setpoint = 2800.0 + 600.0 * np.sin(2 * np.pi * (t - 15.0) / 10.0)
         elif t < 32.0:
-            n_setpoint = 3500.0
+            # 斜坡上升 + 正弦波动
+            base = 2200.0 + 1300.0 * (t - 22.0) / 10.0  # 斜坡上升
+            n_setpoint = base + 300.0 * np.sin(2 * np.pi * (t - 22.0) / 8.0)
         elif t < 42.0:
+            # 阶跃到3800
             n_setpoint = 3800.0
         elif t < 52.0:
-            n_setpoint = 2800.0
+            # 斜坡下降 + 正弦波动
+            base = 3800.0 - 1000.0 * (t - 42.0) / 10.0  # 斜坡下降
+            n_setpoint = base + 200.0 * np.sin(2 * np.pi * (t - 42.0) / 6.0)
         elif t < 62.0:
-            n_setpoint = 3000.0
+            # 梯形波：先上升再保持
-        elif t < 72.0:
+            if t < 55.0:
-            n_setpoint = 4000.0
+                n_setpoint = 2800.0 + 400.0 * (t - 52.0) / 3.0
-        elif t < 82.0:
-            n_setpoint = 4200.0
-        elif t < 92.0:
-            n_setpoint = 3200.0
             else:
-            n_setpoint = 3000.0
+                n_setpoint = 3200.0
+        elif t < 72.0:
+            # 正弦波动：3000 ± 1000 RPM
+            n_setpoint = 3000.0 + 1000.0 * np.sin(2 * np.pi * (t - 62.0) / 10.0)
+        elif t < 82.0:
+            # 斜坡上升
+            n_setpoint = 3000.0 + 600.0 * (t - 72.0) / 10.0
+        elif t < 92.0:
+            # 衰减正弦波
+            amplitude = 400.0 * np.exp(-(t - 82.0) / 5.0)
+            n_setpoint = 3200.0 + amplitude * np.sin(2 * np.pi * (t - 82.0) / 8.0)
+        else:
+            # 斜坡下降到3000
+            n_setpoint = 3200.0 - 200.0 * (t - 92.0) / 8.0
 
         # 外部转矩扰动 - 模拟发动机并联/涡轴输出变化
         if t < 5.0:
@@ -1096,38 +885,61 @@ def run_motor_battery_coupled_test() -> Dict[str, Any]:
         else:
             t_ext = 0.0
 
-        # 负载转矩 - 模拟风速/气压变化
+        # 螺旋桨负载模型：气动阻力矩 + 惯性负载
-        if t < 10.0:
+        # 气动阻力: T = k_drag * n² (与转速平方成正比)
-            t_load = 20.0
+        # 惯性负载: T = J_prop * alpha (与加速度成正比，模拟螺旋桨惯性)
-        elif t < 22.0:
+        n_current = n_setpoint  # 使用目标转速计算负载
-            t_load = 80.0
+        w_current = n_current * 2 * np.pi / 60.0  # rad/s
-        elif t < 32.0:
-            t_load = 40.0
-        elif t < 42.0:
-            t_load = 120.0
-        elif t < 52.0:
-            t_load = 50.0
-        elif t < 62.0:
-            t_load = 90.0
-        elif t < 72.0:
-            t_load = 60.0
-        elif t < 82.0:
-            t_load = 130.0
-        else:
-            t_load = 70.0
 
-        # 外部电功率扰动
+        # 计算角加速度 (rad/s²)
-        if 35.0 < t < 40.0:
+        alpha = (w_current - w_prev) / dt if dt > 0 else 0.0
+
+        # 气动阻力矩 (与转速平方成正比)
+        t_aero = k_drag * n_current ** 2 + T_min
+        # 惯性负载 (与加速度成正比)
+        t_inertia = J_prop * alpha
+
+        t_load = t_aero + t_inertia
+
+        # 更新上一时刻转速
+        w_prev = w_current
+
+        # 外部电功率扰动 (发动机/燃气轮机发电功率注入，正值表示充电，负值表示负载)
+        if 5.0 < t < 10.0:
+            p_ext_elec = 40.0   # 涡轴发电机注入 40kW
+        elif 10.0 < t < 15.0:
+            p_ext_elec = 50.0   # 涡轴发电机注入 50kW
+        elif 20.0 < t < 23.0:
+            p_ext_elec = 60.0   # 涡轴发电机注入 60kW
+        elif 25.0 < t < 30.0:
+            p_ext_elec = 70.0   # 涡轴发电机注入 70kW
+        elif 32.0 < t < 35.0:
+            p_ext_elec = 55.0   # 涡轴发电机注入 55kW
+        elif 35.0 < t < 40.0:
             p_ext_elec = 80.0   # 涡轴发电机注入 80kW
+        elif 45.0 < t < 48.0:
+            p_ext_elec = 90.0   # 涡轴发电机注入 90kW (短时高功率充电)
+        elif 50.0 < t < 53.0:
+            p_ext_elec = 45.0   # 涡轴发电机注入 45kW
         elif 55.0 < t < 60.0:
-            p_ext_elec = -100.0  # 其他设备用电 100kW
+            p_ext_elec = -100.0  # 其他设备用电 100kW (负载)
+        elif 62.0 < t < 65.0:
+            p_ext_elec = 50.0   # 涡轴发电机注入 50kW
+        elif 65.0 < t < 70.0:
+            p_ext_elec = 65.0   # 涡轴发电机注入 65kW
+        elif 72.0 < t < 75.0:
+            p_ext_elec = 55.0   # 涡轴发电机注入 55kW
         elif 75.0 < t < 80.0:
             p_ext_elec = 60.0   # 涡轴发电机注入 60kW
+        elif 82.0 < t < 85.0:
+            p_ext_elec = 70.0   # 涡轴发电机注入 70kW
+        elif 88.0 < t < 95.0:
+            p_ext_elec = 75.0   # 涡轴发电机注入 75kW
 
         # 母线电压波动
         v_bus = battery.V_t + 15.0 * np.sin(2 * np.pi * t / 8.0) + 5.0 * np.sin(2 * np.pi * t / 3.0)
 
-        # 延迟一拍的实际电能参与当前动态步，进而PID在内环打出对下一拍的请求
+        # 延迟一拍的实际电能参与当前动态步，进而MPC在内环打出对下一拍的请求
         state = motor.step(dt, n_setpoint, p_actual_kw, v_bus, t_load, t_ext)
         p_req_kw = state['p_bus_req_kw']
 
@@ -1174,7 +986,7 @@ def run_motor_battery_coupled_test() -> Dict[str, Any]:
     axes[0].legend()
 
     # 2. 转矩响应
-    axes[1].plot(time_array, t_motor_log, 'r-', lw=1.6, label='Motor Torque (PID Output)')
+    axes[1].plot(time_array, t_motor_log, 'r-', lw=1.6, label='Motor Torque (MPC Output)')
     axes[1].plot(time_array, np.array(t_load_log), 'k--', lw=1.2, label='Propeller Load Torque')
     axes[1].plot(time_array, np.array(t_ext_log), color='tab:purple', lw=1.2, label='External Torque')
     axes[1].axhline(0.0, color='k', lw=0.6)
@@ -1210,7 +1022,7 @@ def run_motor_battery_coupled_test() -> Dict[str, Any]:
     axes[4].legend()
 
     plt.tight_layout()
-    plt.savefig("figures/test_complex_output.png")
+    plt.savefig("figures/test_complex_output.png", dpi=300, bbox_inches='tight')
     print("Plot saved to figures/test_complex_output.png")
 
     return {

Model/src/mpc_controller.py

@@ -0,0 +1,298 @@
+import numpy as np
+
+
+# ==============================================================================
+# Pure-Python projected gradient descent for box-constrained optimization
+# (替代 scipy.optimize.minimize SLSQP，避免 Fortran ABI 兼容性问题)
+# ==============================================================================
+def _minimize_box(objective, x0, bounds, lr=1.0, max_iter=80, ftol=1e-6):
+    """
+    Projected gradient descent with Armijo backtracking line search.
+    For small-to-medium MPC horizons (H ≤ 30) this is fast enough.
+    """
+    n = len(x0)
+    lb = np.array([b[0] for b in bounds], dtype=np.float64)
+    ub = np.array([b[1] for b in bounds], dtype=np.float64)
+    x = np.clip(np.array(x0, dtype=np.float64), lb, ub)
+
+    eps = 1e-5  # finite-difference step
+    f_prev = objective(x)
+
+    for _it in range(max_iter):
+        # Approximate gradient via central differences
+        grad = np.empty(n, dtype=np.float64)
+        for i in range(n):
+            x_p = x.copy(); x_p[i] += eps
+            x_m = x.copy(); x_m[i] -= eps
+            grad[i] = (objective(x_p) - objective(x_m)) / (2 * eps)
+
+        # Backtracking line search (Armijo condition)
+        step = lr
+        for _ in range(12):
+            x_new = np.clip(x - step * grad, lb, ub)
+            f_new = objective(x_new)
+            if f_new < f_prev - 1e-4 * step * np.dot(grad, x - x_new):
+                break
+            step *= 0.5
+        else:
+            x_new = np.clip(x - step * grad, lb, ub)
+            f_new = objective(x_new)
+
+        if abs(f_prev - f_new) < ftol:
+            x = x_new
+            break
+        x = x_new
+        f_prev = f_new
+
+    class _Result:
+        pass
+    res = _Result()
+    res.x = x
+    res.fun = f_prev
+    res.success = True
+    return res
+
+# ==============================================================================
+# 1. 涡轴发动机 MPC 控制器 (基于 Scipy SLSQP 高速求解)
+# ==============================================================================
+class TurboShaftMPCController:
+    def __init__(self, tau_fuel, K_inertia, dt=0.02, horizon=15,
+                 min_fuel=10.0, max_fuel=600.0, overshoot_limit=0.05):
+        """
+        初始化高速 MPC 控制器 (基于 Scipy SLSQP)
+        替代 GEKKO 以消除文件 I/O 开销，提升单步推理速度 50x 以上。
+
+        :param overshoot_limit: 超调量硬约束 (0.05 = 5%)
+        """
+        self.dt = dt
+        self.H = horizon
+        self.tau_fuel = tau_fuel
+        self.K_inertia = K_inertia
+        self.min_fuel = min_fuel
+        self.max_fuel = max_fuel
+
+        # 权重参数 (与 GEKKO 版本保持一致)
+        self.W_power = 200.0  # Power Setpoint Weight
+        self.W_dcost = 1.5    # Delta Control Weight (DCOST)
+
+        # 超调量约束
+        self.overshoot_limit = overshoot_limit  # 5% 硬约束
+
+        # 超调惩罚权重 (使用很大的值使约束"硬"化)
+        self.W_overshoot_penalty = 1e6
+
+        # 状态缓存
+        self.last_u = 100.0
+        self.last_N = 0.0
+
+    def reset(self, initial_output, initial_N):
+        self.last_u = initial_output
+        self.last_N = initial_N
+
+    def compute(self, current_N, current_Wfact, target_power, engine_model=None, H_env=0, Ma_env=0, precalc_params=None, **kwargs):
+        """
+        计算 MPC 控制律
+        :param precalc_params: (必须) 元组 (Wf_req_0, Power_0, k_wf, k_p)。
+                               FastMPC 必须配合 Batch Prediction 使用。
+        """
+        if precalc_params is not None:
+            Wf_req_0, Power_0, k_wf, k_p = precalc_params
+        else:
+            raise ValueError("FastMPC 必须配合 Batch Prediction 使用 (提供 precalc_params)")
+
+        N0 = current_N
+        Wf_act0 = current_Wfact
+        N_ref = N0  # 用于线性化的参考点
+
+        # --- 构建线性预测模型 ---
+        # State x = [N, Wf_act]
+        # x_{k+1} = A x_k + B u_k + d
+        # N_{k+1} = N_k + dt * K * (Wf_act_k - (Wf_req_0 + k_wf*(N_k - N0)))
+        #         = (1 - dt*K*k_wf) N_k + (dt*K) Wf_act_k + dt*K*(k_wf*N0 - Wf_req_0)
+        # Wf_act_{k+1} = Wf_act_k + dt * (u_k - Wf_act_k) / tau
+        #              = (1 - dt/tau) Wf_act_k + (dt/tau) u_k
+
+        dt = self.dt
+        K = self.K_inertia
+        tau = self.tau_fuel
+
+        A = np.array([
+            [1 - dt * K * k_wf, dt * K],
+            [0, 1 - dt / tau]
+        ])
+        B = np.array([0, dt / tau])
+        d = np.array([dt * K * (k_wf * N_ref - Wf_req_0), 0])
+
+        # Power Output: P = P0 + k_p * (N - N0)
+        #                 = k_p * N + (P0 - k_p * N0)
+        C_p = k_p
+        D_p = Power_0 - k_p * N_ref
+
+        # --- 超调量硬约束 ---
+        # 升功率时: P_k <= target_power * (1 + overshoot_limit)
+        # 降功率时: P_k >= target_power * (1 - overshoot_limit)
+        P_max = target_power * (1 + self.overshoot_limit)
+        P_min = target_power * (1 - self.overshoot_limit)
+
+        # 判断是升功率还是降功率
+        is_ramping_up = target_power > Power_0
+
+        # --- 优化目标函数 ---
+        # Variables: U = [u_0, ..., u_{H-1}]
+        # x_0 is fixed.
+        # Cost = sum_{k=1 to H} W_power * (P_k - P_target)^2 + sum_{k=0 to H-1} W_dcost * (u_k - u_{k-1})^2
+        #        + W_overshoot_penalty * (违反约束的惩罚)
+
+        def objective(U):
+            cost = 0.0
+            x_k = np.array([N0, Wf_act0])
+            u_prev = self.last_u  # Use last commanded u for the first delta
+
+            for k in range(self.H):
+                u_k = U[k]
+
+                # Dynamics Step
+                x_k = A @ x_k + B * u_k + d
+
+                # Output
+                P_k = C_p * x_k[0] + D_p
+
+                # Cost Accumulation
+                cost += self.W_power * (P_k - target_power) ** 2
+                cost += self.W_dcost * (u_k - u_prev) ** 2
+
+                # 超调惩罚: 如果违反约束，添加巨大惩罚
+                if is_ramping_up:
+                    if P_k > P_max:
+                        cost += self.W_overshoot_penalty * (P_k - P_max) ** 2
+                else:
+                    if P_k < P_min:
+                        cost += self.W_overshoot_penalty * (P_min - P_k) ** 2
+
+                u_prev = u_k
+
+            return cost
+
+        # --- 求解 ---
+        # 初始猜测: 保持上一次的输入
+        U0 = np.full(self.H, float(self.last_u), dtype=np.float64)
+
+        # 约束: lb <= u <= ub
+        bounds = [(self.min_fuel, self.max_fuel) for _ in range(self.H)]
+
+        # 使用纯Python投影梯度下降求解带约束优化 (替代 SLSQP)
+        res = _minimize_box(objective, U0, bounds, lr=5.0, max_iter=60, ftol=1e-3)
+
+        # 更新状态
+        u_opt = res.x[0]
+        self.last_u = u_opt
+
+        return u_opt
+
+
+# ==============================================================================
+# 2. 驱动电机 MPC 控制器 (基于 Scipy SLSQP 高速求解)
+# ==============================================================================
+class MotorMPCController:
+    def __init__(self, J, B_visc, dt=0.02, horizon=10, W_speed=100.0, W_dcost=1.0, overshoot_limit=0.05):
+        """
+        初始化高速电机 MPC 控制器 (基于 Scipy SLSQP)
+        替代 GEKKO 以消除文件 I/O 开销，提升单步推理速度 50x 以上。
+
+        :param overshoot_limit: 超调量硬约束 (0.05 = 5%)
+        """
+        self.dt = dt
+        self.H = horizon
+        self.J = J
+        self.B_visc = B_visc
+
+        # 权重参数 (可调)
+        self.W_speed = W_speed  # 转速跟踪权重
+        self.W_dcost = W_dcost  # 控制变化惩罚
+
+        # 超调量约束
+        self.overshoot_limit = overshoot_limit  # 5% 硬约束
+
+        # 超调惩罚权重 (使用很大的值使约束"硬"化)
+        self.W_overshoot_penalty = 1e6
+
+        # 状态缓存
+        self.last_T_cmd = 0.0
+
+    def reset(self, initial_w=0.0):
+        """重新初始化状态"""
+        self.last_T_cmd = 0.0
+
+    def compute(self, current_w, target_w, t_load, t_ext, t_lim_upper, t_lim_lower):
+        """
+        执行单步 MPC 优化计算
+        :param current_w: 当前实际转速 (rad/s)
+        :param target_w: 目标转速 (rad/s)
+        :param t_load: 当前气动负载转矩 (Nm)
+        :param t_ext: 当前外部轴系转矩 (Nm)
+        :param t_lim_upper: 当前母线电压下，电机能发出的【正向转矩上限】(发电极限)
+        :param t_lim_lower: 当前母线电压下，电机能发出的【负向转矩下限】(驱动极限)
+        :return: 最优转矩指令 (Nm)
+        """
+        w0 = current_w
+
+        dt = self.dt
+        J = self.J
+        B = self.B_visc
+
+        # --- 离散线性模型 ---
+        # w_{k+1} = w_k + dt * (-(T_cmd + T_ext + T_load + B*w_k) / J)
+        #          = (1 - dt*B/J) * w_k - dt/J * T_cmd - dt/J * (T_ext + T_load)
+        a = 1 - dt * B / J
+        b = -dt / J
+        d = -dt / J * (t_ext + t_load)
+
+        # --- 超调量硬约束 ---
+        # 升速时: w_k <= target_w * (1 + overshoot_limit)
+        # 降速时: w_k >= target_w * (1 - overshoot_limit)
+        w_max = target_w * (1 + self.overshoot_limit)
+        w_min = target_w * (1 - self.overshoot_limit)
+
+        # 判断是升速还是降速
+        is_ramping_up = target_w > w0
+
+        # --- 优化目标函数 ---
+        # Cost = sum_{k=1 to H} W_speed * (w_k - target_w)^2 + sum_{k=0 to H-1} W_dcost * (T_k - T_{k-1})^2
+        #        + W_overshoot_penalty * (违反约束的惩罚)
+
+        def objective(U):
+            cost = 0.0
+            w_k = w0
+            T_prev = self.last_T_cmd
+
+            for k in range(self.H):
+                T_k = U[k]
+                w_k = a * w_k + b * T_k + d  # 动力学迭代
+                cost += self.W_speed * (w_k - target_w) ** 2
+                cost += self.W_dcost * (T_k - T_prev) ** 2
+
+                # 超调惩罚: 如果违反约束，添加巨大惩罚
+                if is_ramping_up:
+                    if w_k > w_max:
+                        cost += self.W_overshoot_penalty * (w_k - w_max) ** 2
+                else:
+                    if w_k < w_min:
+                        cost += self.W_overshoot_penalty * (w_min - w_k) ** 2
+
+                T_prev = T_k
+
+            return cost
+
+        # 初始猜测
+        U0 = np.full(self.H, float(self.last_T_cmd), dtype=np.float64)
+
+        # 约束: 转矩边界
+        bounds = [(t_lim_lower, t_lim_upper) for _ in range(self.H)]
+
+        # 求解 (使用纯Python投影梯度下降，替代 SLSQP)
+        res = _minimize_box(objective, U0, bounds, lr=50.0, max_iter=120, ftol=1e-5)
+
+        T_opt = res.x[0]
+        self.last_T_cmd = T_opt
+
+        return T_opt

Model/src/series_hybrid_sim.py

@@ -1,6 +1,6 @@
-import os
 import numpy as np
 import matplotlib.pyplot as plt
+from tqdm import tqdm
 
 from engine_dynamic_sim import TurboshaftDynamicSim
 from motor_sim import MotorSim
@@ -10,25 +10,15 @@ class SeriesHybridSystem:
     """
     串联式混合动力系统总成
     """
-    def __init__(self):
+    def __init__(self, mpc_overshoot_limit: float = 0.05):
-        # ===== 新增：按Model目录定位GPR数据与权重，避免从主项目调用时路径错误 =====
+        self.genset = TurboshaftDynamicSim(mpc_overshoot_limit=mpc_overshoot_limit)
-        model_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-        gpr_csv_path = os.path.join(model_root, "data", "Cleaned_Engine_Data_Full.csv")
-        gpr_pth_path = os.path.join(model_root, "data", "engine_gpr_model.pth")
-        self.genset = TurboshaftDynamicSim(
-            gpr_csv_path=gpr_csv_path,
-            gpr_pth_path=gpr_pth_path,
-            kp=0.5,
-            ki=0.5,
-            kd=0.0
-        )
         self.drive_motor = MotorSim(
             P_rate=300e3,
             w_rate=575.95,
-            k_p_w=5.0,   # 增大比例增益
-            k_i_w=2.0,   # 增大积分增益
-            k_d_w=0.5,   # 适度微分
             J=1.0,
+            mpc_W_speed=40,
+            mpc_W_dcost=5,
+            mpc_overshoot_limit=mpc_overshoot_limit,  # 超调量硬约束 (5%)
         )
         self.battery = BatterySim(capacity_kwh=50.0, initial_soc=0.6)
 
@@ -172,7 +162,7 @@ if __name__ == "__main__":
                            'p_batt_actual_kw', 'wf_kg_h']}
     
     print("-> 开始全系统闭环步进仿真 (总时长 3 分钟)...")
-    for t in time_array:
+    for t in tqdm(time_array, desc='Simulating', unit='step'):
         # 3分钟测试剖面
         if t < 15.0:
             target_rpm, load_torque = 1500.0, 50.0   # 地面滑行
@@ -204,7 +194,7 @@ if __name__ == "__main__":
 
     # 2. 功率分配流向
     axes[1].plot(time_array, log['p_drive_req_kw'], 'k--', lw=1.5, label='Drive Motor Request')
-    axes[1].plot(time_array, log['p_engine_out_kw'], 'r-', lw=1.5, label='Engine Output (APU)')
+    axes[1].plot(time_array, log['p_engine_out_kw'], 'r-', lw=1.5, label='Engine Output (Turbo Shaft)')
     axes[1].plot(time_array, log['p_batt_actual_kw'], 'g-', lw=1.5, label='Battery Output')
     axes[1].set_ylabel('Power [kW]')
     axes[1].set_title('System Power Flow (Energy Management)')
@@ -249,7 +239,7 @@ if __name__ == "__main__":
         os.makedirs(data_dir)
     dat_path = os.path.join(data_dir, 'series_hybrid_data.dat')
     
-    with open(dat_path, 'w') as f:
+    with open(dat_path, 'w', encoding='utf-8') as f:
         f.write('# Time(s)\tTarget_RPM\tActual_RPM\tError_RPM\tSOC\tEngine_Power\tDrive_Req\tBatt_Power\n')
         for i in range(len(time_array)):
             err = log['target_prop_rpm'][i] - log['prop_speed_rpm'][i]

app.py

@@ -1,3 +1,4 @@
+import os
 import gradio as gr
 import time
 from functools import partial
@@ -10,8 +11,8 @@ from analysis_functions import (
     frequency_domain_analysis,
     root_locus_analysis
 )
-# ===== 新增：算例演示模块函数导入 =====
+# ===== 算例演示模块函数导入（四阶段设计）=====
-from case_demo_functions import run_case_demo
+from case_demo_functions import run_distillation_demo, run_gpr_training, run_engine_design, run_motor_design, run_hybrid_demo
 from chatbot import chat_with_ai
 from user_stats import get_online_status_html, update_user_activity
 from ui_components import (
@@ -23,8 +24,98 @@ from ui_components import (
     create_chatbot_tab
 )
 
+
+# ===== 系统资源监控 =====
+def get_system_monitor_html():
+    """获取 CPU / 内存 / GPU 使用率的 HTML 小组件"""
+    try:
+        import psutil
+        cpu_pct = psutil.cpu_percent(interval=0)
+        mem = psutil.virtual_memory()
+        mem_pct = mem.percent
+        mem_used_gb = mem.used / (1024 ** 3)
+        mem_total_gb = mem.total / (1024 ** 3)
+    except ImportError:
+        return "<div style='text-align:center;color:#999;font-size:0.8em;'>psutil 未安装，无法监控系统资源</div>"
+
+    # GPU 信息 — 优先用 nvidia-smi（不依赖 PyTorch CUDA 版本），再用 torch.cuda 兜底
+    gpu_html = ""
+    try:
+        import subprocess as _sp
+        _r = _sp.run(
+            ['nvidia-smi', '--query-gpu=name,memory.used,memory.total,utilization.gpu',
+             '--format=csv,noheader,nounits'],
+            capture_output=True, text=True, timeout=3
+        )
+        if _r.returncode == 0 and _r.stdout.strip():
+            _parts = [p.strip() for p in _r.stdout.strip().split('\n')[0].split(',')]
+            _gpu_mem_used = float(_parts[1]) / 1024   # MiB → GiB
+            _gpu_mem_total = float(_parts[2]) / 1024
+            _gpu_util = float(_parts[3])
+            _gc = '#ff6b6b' if _gpu_util > 80 else '#ffd93d' if _gpu_util > 50 else '#6bcb77'
+            gpu_html = (
+                f"<div style='display:inline-flex;align-items:center;gap:6px;'>"
+                f"<span>🎮 GPU</span>"
+                f"<div style='width:90px;height:8px;background:#444;border-radius:4px;overflow:hidden;'>"
+                f"<div style='width:{min(_gpu_util, 100):.0f}%;height:100%;background:{_gc};'></div>"
+                f"</div>"
+                f"<span>{_gpu_util:.0f}% &nbsp; {_gpu_mem_used:.1f}/{_gpu_mem_total:.0f} GB</span>"
+                f"</div>"
+            )
+        else:
+            raise RuntimeError("nvidia-smi no output")
+    except Exception:
+        try:
+            import torch as _torch
+            if _torch.cuda.is_available():
+                _mem_alloc = _torch.cuda.memory_allocated(0) / (1024 ** 3)
+                _mem_total = _torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)
+                _util = _mem_alloc / max(_mem_total, 0.01) * 100
+                _gc = '#ff6b6b' if _util > 80 else '#ffd93d' if _util > 50 else '#6bcb77'
+                gpu_html = (
+                    f"<div style='display:inline-flex;align-items:center;gap:6px;'>"
+                    f"<span>🎮 GPU</span>"
+                    f"<div style='width:90px;height:8px;background:#444;border-radius:4px;overflow:hidden;'>"
+                    f"<div style='width:{min(_util, 100):.0f}%;height:100%;background:{_gc};'></div>"
+                    f"</div>"
+                    f"<span>{_mem_alloc:.1f}/{_mem_total:.0f} GB</span>"
+                    f"</div>"
+                )
+            else:
+                gpu_html = "<div style='display:inline-flex;align-items:center;gap:4px;'><span>🎮 GPU N/A</span></div>"
+        except Exception:
+            gpu_html = "<div style='display:inline-flex;align-items:center;gap:4px;'><span>🎮 GPU N/A</span></div>"
+
+    cpu_color = '#ff6b6b' if cpu_pct > 80 else '#ffd93d' if cpu_pct > 50 else '#6bcb77'
+    mem_color = '#ff6b6b' if mem_pct > 80 else '#ffd93d' if mem_pct > 50 else '#6bcb77'
+
+    html = (
+        f"<div style='display:flex;justify-content:center;gap:20px;flex-wrap:wrap;"
+        f"font-size:0.82em;color:#ddd;padding:4px 10px;'>"
+        # CPU
+        f"<div style='display:inline-flex;align-items:center;gap:6px;'>"
+        f"<span>🖥️ CPU</span>"
+        f"<div style='width:90px;height:8px;background:#444;border-radius:4px;overflow:hidden;'>"
+        f"<div style='width:{min(cpu_pct, 100):.0f}%;height:100%;background:{cpu_color};'></div>"
+        f"</div>"
+        f"<span>{cpu_pct:.0f}%</span>"
+        f"</div>"
+        # Memory
+        f"<div style='display:inline-flex;align-items:center;gap:6px;'>"
+        f"<span>💾 RAM</span>"
+        f"<div style='width:90px;height:8px;background:#444;border-radius:4px;overflow:hidden;'>"
+        f"<div style='width:{min(mem_pct, 100):.0f}%;height:100%;background:{mem_color};'></div>"
+        f"</div>"
+        f"<span>{mem_used_gb:.1f}/{mem_total_gb:.0f} GB ({mem_pct:.0f}%)</span>"
+        f"</div>"
+        # GPU
+        f"{gpu_html}"
+        f"</div>"
+    )
+    return html
+
 # 加载外部CSS文件
-with open("assets/styles.css", "r", encoding="utf-8") as f:
+with open(os.path.join(os.path.dirname(__file__), "assets", "styles.css"), "r", encoding="utf-8") as f:
     custom_css = f.read()
 
 # --- 主应用界面 ---
@@ -36,23 +127,8 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
     # 创建头部信息和在线计数器
     online_counter = create_header()
 
-    # 创建共享的输入组件
+    # 系统资源监控（始终可见）
-    with gr.Row():
+    system_monitor = gr.HTML(value=get_system_monitor_html, elem_id="system-monitor")
-        with gr.Column(scale=1):
-             with gr.Group():
-                gr.HTML("<div class='card-title'>📊 通用系统参数</div>")
-                num_input = gr.Textbox(
-                    label="传递函数分子系数 (Numerator)",
-                    value="1",
-                    placeholder="例如: 1 或 1,2,3",
-                    info="💡 用逗号分隔多个系数，从最高次项到常数项"
-                )
-                den_input = gr.Textbox(
-                    label="传递函数分母系数 (Denominator)",
-                    value="1,6,11,6",
-                    placeholder="例如: 1,2,1",
-                    info="💡 分母阶数通常高于或等于分子阶数"
-                )
 
     # 创建功能选项卡
     with gr.Tabs() as tabs:
@@ -80,13 +156,13 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
     # --- 时域分析事件 ---
     time_domain_ui["confirm_button"].click(
         fn=display_transfer_function,
-        inputs=[num_input, den_input],
+        inputs=[time_domain_ui["num_input"], time_domain_ui["den_input"]],
         outputs=[time_domain_ui["tf_display"]]
     ).then(lambda: get_online_status_html(), outputs=online_counter)
 
     time_domain_ui["analyze_button"].click(
         fn=time_domain_analysis,
-        inputs=[num_input, den_input],
+        inputs=[time_domain_ui["num_input"], time_domain_ui["den_input"]],
         outputs=[time_domain_ui["output_plot"], time_domain_ui["output_metrics"]]
     ).then(lambda: get_online_status_html(), outputs=online_counter)
 
@@ -96,7 +172,7 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
         fig, metrics, tf_latex, stability = frequency_domain_analysis(num, den, k)
         return fig, metrics, tf_latex, stability, k, get_online_status_html()
 
-    freq_inputs = [num_input, den_input, freq_domain_ui["log_k_slider"]]
+    freq_inputs = [freq_domain_ui["num_input"], freq_domain_ui["den_input"], freq_domain_ui["log_k_slider"]]
     freq_outputs = [
         freq_domain_ui["plot_output"],
         freq_domain_ui["metrics_display"],
@@ -116,7 +192,7 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
         fig, poles, k_val = root_locus_analysis(num, den, log_k)
         return fig, poles, k_val, get_online_status_html()
 
-    rl_inputs = [root_locus_ui["log_k_slider"], num_input, den_input]
+    rl_inputs = [root_locus_ui["log_k_slider"], root_locus_ui["num_input"], root_locus_ui["den_input"]]
     rl_outputs = [
         root_locus_ui["plot_output"],
         root_locus_ui["poles_display"],
@@ -129,59 +205,170 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
         outputs=rl_outputs
     )
     
-    # 当输入框变化时，也更新频域和根轨迹（如果它们是当前可见的）
+    # 频域：当传递函数输入框变化时自动更新
-    def update_all_on_tf_change(num, den, log_k_freq, log_k_rl):
+    freq_domain_ui["num_input"].change(
-        # 更新频域
+        fn=update_frequency_analysis_wrapper,
-        k_freq = 10**log_k_freq
+        inputs=freq_inputs, outputs=freq_outputs
-        fig_freq, metrics, tf_latex, stability = frequency_domain_analysis(num, den, k_freq)
+    )
-        
+    freq_domain_ui["den_input"].change(
-        # 更新根轨迹
+        fn=update_frequency_analysis_wrapper,
-        fig_rl, poles, k_val_rl = root_locus_analysis(num, den, log_k_rl)
+        inputs=freq_inputs, outputs=freq_outputs
-
-        return (
-            fig_freq, metrics, tf_latex, stability, k_freq,
-            fig_rl, poles, k_val_rl,
-            get_online_status_html()
     )
 
-    tf_change_inputs = [num_input, den_input, freq_domain_ui["log_k_slider"], root_locus_ui["log_k_slider"]]
+    # 根轨迹：当传递函数输入框变化时自动更新
-    tf_change_outputs = freq_outputs[:-1] + rl_outputs[:-1] + [online_counter]
+    root_locus_ui["num_input"].change(
+        fn=update_rl_view_wrapper,
+        inputs=rl_inputs, outputs=rl_outputs
+    )
+    root_locus_ui["den_input"].change(
+        fn=update_rl_view_wrapper,
+        inputs=rl_inputs, outputs=rl_outputs
+    )
 
-    num_input.change(fn=update_all_on_tf_change, inputs=tf_change_inputs, outputs=tf_change_outputs)
+    # ===== 算例演示事件绑定（四阶段）=====
-    den_input.change(fn=update_all_on_tf_change, inputs=tf_change_inputs, outputs=tf_change_outputs)
 
-    # ===== 新增：算例演示事件包装器 =====
+    # --- 阶段零-A：GPR 模型训练 ---
-    def run_case_demo_wrapper(sim_time, dt, initial_soc, initial_engine_power, profile, rpm_scale, load_scale, sid):
+    def run_gpr_wrapper(mode, sid, progress=gr.Progress(track_tqdm=True)):
         update_user_activity(sid)
-        fig, summary, table_data = run_case_demo(
+        fig, summary = run_gpr_training(mode=mode, progress=progress)
-            sim_time_s=sim_time,
+        return fig, summary, get_online_status_html()
-            dt=dt,
+
-            initial_soc_pct=initial_soc,
+    case_demo_ui["gpr_run_button"].click(
-            initial_engine_power_kw=initial_engine_power,
+        fn=run_gpr_wrapper,
-            profile_name=profile,
+        inputs=[case_demo_ui["gpr_mode"], session_id],
-            rpm_scale=rpm_scale,
+        outputs=[case_demo_ui["gpr_plot"], case_demo_ui["gpr_summary"], online_counter]
-            load_scale=load_scale
+    )
+
+    # --- 阶段零-B：NN 模型训练（蒸馏）---
+    def run_distillation_wrapper(epochs, lr, hidden, sid, progress=gr.Progress(track_tqdm=True)):
+        update_user_activity(sid)
+        fig, summary = run_distillation_demo(epochs, lr, hidden, progress=progress)
+        return fig, summary, get_online_status_html()
+
+    case_demo_ui["distill_run_button"].click(
+        fn=run_distillation_wrapper,
+        inputs=[
+            case_demo_ui["distill_epochs"], case_demo_ui["distill_lr"],
+            case_demo_ui["distill_hidden"],
+            session_id
+        ],
+        outputs=[case_demo_ui["distill_plot"], case_demo_ui["distill_summary"], online_counter]
+    )
+
+    # --- 阶段一：发动机控制器设计 ---
+    def run_engine_design_wrapper(sim_time, dt, init_power, target_power,
+                                   controller_type,
+                                   kp, ki, kd, tau_fuel, K_inertia,
+                                   mpc_horizon, mpc_W_power, mpc_W_dcost, mpc_overshoot,
+                                   sid, progress=gr.Progress(track_tqdm=True)):
+        update_user_activity(sid)
+        fig, summary = run_engine_design(
+            sim_time, dt, init_power, target_power,
+            controller_type,
+            kp, ki, kd, tau_fuel, K_inertia,
+            mpc_horizon, mpc_W_power, mpc_W_dcost, mpc_overshoot / 100.0,
+            progress=progress
+        )
+        return fig, summary, get_online_status_html()
+
+    case_demo_ui["eng_run_button"].click(
+        fn=run_engine_design_wrapper,
+        inputs=[
+            case_demo_ui["eng_sim_time"], case_demo_ui["eng_dt"],
+            case_demo_ui["eng_init_power"], case_demo_ui["eng_target_power"],
+            case_demo_ui["eng_controller_type"],
+            case_demo_ui["eng_kp"], case_demo_ui["eng_ki"], case_demo_ui["eng_kd"],
+            case_demo_ui["eng_tau_fuel"], case_demo_ui["eng_K_inertia"],
+            case_demo_ui["eng_mpc_horizon"], case_demo_ui["eng_mpc_W_power"],
+            case_demo_ui["eng_mpc_W_dcost"], case_demo_ui["eng_mpc_overshoot"],
+            session_id
+        ],
+        outputs=[case_demo_ui["eng_plot"], case_demo_ui["eng_summary"], online_counter]
+    )
+
+    # --- 阶段二：电机控制器设计 ---
+    def run_motor_design_wrapper(sim_time, dt, target_rpm, load_torque,
+                                  controller_type,
+                                  kp, ki, kd, J,
+                                  mpc_W_speed, mpc_W_dcost, mpc_overshoot,
+                                  sid, progress=gr.Progress(track_tqdm=True)):
+        update_user_activity(sid)
+        fig, summary = run_motor_design(
+            sim_time, dt, target_rpm, load_torque,
+            controller_type,
+            kp, ki, kd, J,
+            mpc_W_speed, mpc_W_dcost, mpc_overshoot / 100.0,
+            progress=progress
+        )
+        return fig, summary, get_online_status_html()
+
+    case_demo_ui["mot_run_button"].click(
+        fn=run_motor_design_wrapper,
+        inputs=[
+            case_demo_ui["mot_sim_time"], case_demo_ui["mot_dt"],
+            case_demo_ui["mot_target_rpm"], case_demo_ui["mot_load_torque"],
+            case_demo_ui["mot_controller_type"],
+            case_demo_ui["mot_kp"], case_demo_ui["mot_ki"], case_demo_ui["mot_kd"],
+            case_demo_ui["mot_J"],
+            case_demo_ui["mot_mpc_W_speed"], case_demo_ui["mot_mpc_W_dcost"],
+            case_demo_ui["mot_mpc_overshoot"],
+            session_id
+        ],
+        outputs=[case_demo_ui["mot_plot"], case_demo_ui["mot_summary"], online_counter]
+    )
+
+    # --- 阶段三：能量管理策略设计（自动引用前两阶段控制器参数）---
+    def run_hybrid_demo_wrapper(sim_time, dt, initial_soc, initial_engine_power,
+                                 profile,
+                                 eng_ctrl_type, eng_kp, eng_ki, eng_kd,
+                                 eng_mpc_horizon, eng_mpc_W_power, eng_mpc_W_dcost, eng_mpc_overshoot,
+                                 mot_ctrl_type, mot_kp, mot_ki, mot_kd, mot_J,
+                                 mot_mpc_W_speed, mot_mpc_W_dcost, mot_mpc_overshoot,
+                                 soc_target, soc_low, soc_high,
+                                 p_eng_min, p_eng_max, p_charge, k_soc,
+                                 power_reserve, battery_capacity, sid,
+                                 progress=gr.Progress(track_tqdm=True)):
+        update_user_activity(sid)
+        fig, summary, table_data = run_hybrid_demo(
+            sim_time, dt, initial_soc, initial_engine_power, profile,
+            eng_ctrl_type, eng_kp, eng_ki, eng_kd,
+            eng_mpc_horizon, eng_mpc_W_power, eng_mpc_W_dcost, eng_mpc_overshoot / 100.0,
+            mot_ctrl_type, mot_kp, mot_ki, mot_kd, mot_J,
+            mot_mpc_W_speed, mot_mpc_W_dcost, mot_mpc_overshoot / 100.0,
+            soc_target, soc_low, soc_high,
+            p_eng_min, p_eng_max, p_charge, k_soc,
+            power_reserve, battery_capacity,
+            progress=progress
         )
         return fig, summary, table_data, get_online_status_html()
 
-    # ===== 新增：算例演示按钮事件绑定 =====
+    case_demo_ui["hybrid_run_button"].click(
-    case_demo_ui["run_button"].click(
+        fn=run_hybrid_demo_wrapper,
-        fn=run_case_demo_wrapper,
         inputs=[
-            case_demo_ui["sim_time"],
+            case_demo_ui["sim_time"], case_demo_ui["dt"],
-            case_demo_ui["dt"],
+            case_demo_ui["initial_soc"], case_demo_ui["initial_engine_power"],
-            case_demo_ui["initial_soc"],
-            case_demo_ui["initial_engine_power"],
             case_demo_ui["profile"],
-            case_demo_ui["rpm_scale"],
+            # 发动机控制器参数
-            case_demo_ui["load_scale"],
+            case_demo_ui["eng_controller_type"],
+            case_demo_ui["eng_kp"], case_demo_ui["eng_ki"], case_demo_ui["eng_kd"],
+            case_demo_ui["eng_mpc_horizon"], case_demo_ui["eng_mpc_W_power"],
+            case_demo_ui["eng_mpc_W_dcost"], case_demo_ui["eng_mpc_overshoot"],
+            # 电机控制器参数
+            case_demo_ui["mot_controller_type"],
+            case_demo_ui["mot_kp"], case_demo_ui["mot_ki"], case_demo_ui["mot_kd"],
+            case_demo_ui["mot_J"],
+            case_demo_ui["mot_mpc_W_speed"], case_demo_ui["mot_mpc_W_dcost"],
+            case_demo_ui["mot_mpc_overshoot"],
+            # 能量管理策略参数
+            case_demo_ui["soc_target"], case_demo_ui["soc_low"], case_demo_ui["soc_high"],
+            case_demo_ui["p_eng_min"], case_demo_ui["p_eng_max"],
+            case_demo_ui["p_charge"], case_demo_ui["k_soc"],
+            case_demo_ui["power_reserve"], case_demo_ui["battery_capacity"],
             session_id
         ],
         outputs=[
-            case_demo_ui["plot"],
+            case_demo_ui["hybrid_plot"], case_demo_ui["hybrid_summary"],
-            case_demo_ui["summary"],
+            case_demo_ui["hybrid_table"], online_counter
-            case_demo_ui["table"],
-            online_counter
         ]
     )
 
@@ -215,14 +402,13 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
     )
     
     # --- 页面加载和定时器事件 ---
-    def on_page_load(sid):
+    demo.load(fn=lambda: get_online_status_html(), outputs=[online_counter])
-        update_user_activity(sid)
-        return get_online_status_html()
-
-    demo.load(fn=on_page_load, inputs=[session_id], outputs=[online_counter])
 
     gr.Timer(10).tick(fn=get_online_status_html, outputs=online_counter)
 
+    # 系统资源监控定时刷新（每 3 秒）
+    gr.Timer(3).tick(fn=get_system_monitor_html, outputs=system_monitor)
+
 
 if __name__ == "__main__":
     demo.queue().launch(

data_usage/usage_stats.json

@@ -1,4 +1,4 @@
 {
-  "total_users": 22,
+  "total_users": 37,
-  "last_saved_at": 1775496284.1673155
+  "last_saved_at": 1775560914.9575145
 }

requirements.txt

@@ -1,7 +1,7 @@
 # requirements.txt
 
-# ===== 新增：PyTorch CPU 轮子下载源 =====
+# ===== PyTorch：GPU (CUDA 12.1) 版本，如无 NVIDIA GPU 可改为 cpu =====
---extra-index-url https://download.pytorch.org/whl/cpu
+--extra-index-url https://download.pytorch.org/whl/cu121
 
 gradio==4.44.1
 gradio-client==1.3.0
@@ -14,10 +14,11 @@ matplotlib==3.9.4
 aiohttp==3.13.5
 pillow==10.4.0
 # ===== 新增：混动模型（Model）运行依赖 =====
-torch==2.4.1
+torch==2.4.1+cu121
 botorch==0.14.0
 gpytorch==1.14
 pyro-ppl==1.9.1
 pandas==2.3.3
-scipy==1.15.3
+scipy>=1.10
 scikit-learn==1.7.1
+psutil>=5.9

ui_components.py

@@ -1,8 +1,13 @@
-import gradio as gr
+import gradio as gr
 from assets.knowledge_cards_html import (
     TIME_DOMAIN_KNOWLEDGE,
     FREQUENCY_DOMAIN_KNOWLEDGE,
-    ROOT_LOCUS_KNOWLEDGE
+    ROOT_LOCUS_KNOWLEDGE,
+    ENGINE_CONTROL_KNOWLEDGE,
+    MOTOR_CONTROL_KNOWLEDGE,
+    GPR_KNOWLEDGE,
+    NN_KNOWLEDGE,
+    EMS_KNOWLEDGE,
 )
 
 def create_header():
@@ -66,6 +71,20 @@ def create_time_domain_tab():
     ui_dict = {}
     with gr.Row():
         with gr.Column(scale=1):
+            with gr.Group():
+                gr.HTML("<div class='card-title'>📊 传递函数设定</div>")
+                ui_dict["num_input"] = gr.Textbox(
+                    label="分子系数 (Numerator)",
+                    value="1",
+                    placeholder="例如: 1 或 1,2,3",
+                    info="💡 用逗号分隔，从最高次项到常数项"
+                )
+                ui_dict["den_input"] = gr.Textbox(
+                    label="分母系数 (Denominator)",
+                    value="1,6,11,6",
+                    placeholder="例如: 1,2,1",
+                    info="💡 分母阶数通常 ≥ 分子阶数"
+                )
             with gr.Group():
                 gr.HTML("<div class='card-title'>🔧 系统模型</div>")
                 ui_dict["tf_display"] = gr.Markdown(label="当前传递函数", elem_classes="output-display")
@@ -116,6 +135,20 @@ def create_frequency_domain_tab():
     ui_dict = {}
     with gr.Row():
         with gr.Column(scale=1):
+            with gr.Group():
+                gr.HTML("<div class='card-title'>📊 传递函数设定</div>")
+                ui_dict["num_input"] = gr.Textbox(
+                    label="分子系数 (Numerator)",
+                    value="1",
+                    placeholder="例如: 1 或 1,2,3",
+                    info="💡 用逗号分隔，从最高次项到常数项"
+                )
+                ui_dict["den_input"] = gr.Textbox(
+                    label="分母系数 (Denominator)",
+                    value="1,6,11,6",
+                    placeholder="例如: 1,2,1",
+                    info="💡 分母阶数通常 ≥ 分子阶数"
+                )
             with gr.Group():
                 gr.HTML("<div class='card-title'>🎚️ 调整系统增益</div>")
                 ui_dict["log_k_slider"] = gr.Slider(minimum=-4, maximum=4, value=1, step=0.01, label="对数增益 log₁₀(K)", info="💡 拖动滑块查看实时变化")
@@ -127,6 +160,8 @@ def create_frequency_domain_tab():
                 gr.HTML("<div class='card-title'>📊 稳定裕度分析</div>")
                 ui_dict["metrics_display"] = gr.Textbox(label="Stability Margins", lines=4, interactive=False, elem_classes="output-metrics")
                 ui_dict["stability_display"] = gr.Markdown(elem_classes="stability-result")
+                with gr.Row():
+                    ui_dict["analyze_button"] = gr.Button("🚀 开始分析", variant="primary", scale=1, elem_classes="primary-btn")
         with gr.Column(scale=2):
             ui_dict["plot_output"] = gr.Plot(label="频域响应图", elem_classes="plot-container")
             # 知识卡片
@@ -163,6 +198,20 @@ def create_root_locus_tab():
     ui_dict = {}
     with gr.Row():
         with gr.Column(scale=1):
+            with gr.Group():
+                gr.HTML("<div class='card-title'>📊 传递函数设定</div>")
+                ui_dict["num_input"] = gr.Textbox(
+                    label="分子系数 (Numerator)",
+                    value="1",
+                    placeholder="例如: 1 或 1,2,3",
+                    info="💡 用逗号分隔，从最高次项到常数项"
+                )
+                ui_dict["den_input"] = gr.Textbox(
+                    label="分母系数 (Denominator)",
+                    value="1,6,11,6",
+                    placeholder="例如: 1,2,1",
+                    info="💡 分母阶数通常 ≥ 分子阶数"
+                )
             with gr.Group():
                 gr.HTML("<div class='card-title'>🎚️ 调整系统增益</div>")
                 ui_dict["log_k_slider"] = gr.Slider(minimum=-4, maximum=4, value=1, step=0.01, label="对数增益 log₁₀(K)", info="💡 拖动滑块观察极点移动")
@@ -170,6 +219,8 @@ def create_root_locus_tab():
             with gr.Group():
                 gr.HTML("<div class='card-title'>📍 闭环极点位置</div>")
                 ui_dict["poles_display"] = gr.Textbox(label="Closed-Loop Pole Locations", lines=6, interactive=False, elem_classes="output-metrics")
+                with gr.Row():
+                    ui_dict["analyze_button"] = gr.Button("🚀 开始分析", variant="primary", scale=1, elem_classes="primary-btn")
         with gr.Column(scale=2):
             ui_dict["plot_output"] = gr.Plot(label="根轨迹图")
             gr.HTML(f"""
@@ -201,34 +252,285 @@ def create_root_locus_tab():
     return ui_dict
 
 def create_case_demo_tab():
-    """创建算例演示选项卡的UI组件"""
+    """创建算例演示选项卡 — 四阶段交互设计（蒸馏→发动机→电机→能量管理）"""
     ui_dict = {}
+
+    # === MathJax re-render helper (reused across tabs) ===
+    def _mathjax_script(div_id):
+        return f"""
+        <script>
+            (function() {{
+                function renderMath() {{
+                    setTimeout(function() {{
+                        if (typeof MathJax !== 'undefined' && MathJax.typesetPromise) {{
+                            MathJax.typesetPromise([document.getElementById('{div_id}')])
+                                .catch(function(err) {{ console.log('MathJax error:', err); }});
+                        }} else {{ setTimeout(renderMath, 500); }}
+                    }}, 300);
+                }}
+                if (document.readyState === 'loading') {{
+                    document.addEventListener('DOMContentLoaded', renderMath);
+                }} else {{ renderMath(); }}
+            }})();
+        </script>"""
+
+    with gr.Tabs():
+        # ========== 阶段零：模型训练（GPR + NN 两个子标签页）==========
+        with gr.TabItem("🧬 模型训练", id="distill_tab"):
+            gr.HTML("""<div style='background:linear-gradient(135deg,#f3e5f5,#e1bee7);padding:10px 16px;
+                        border-radius:8px;margin-bottom:10px;font-size:0.92em;color:#6a1b9a;'>
+                <b>阶段零</b>：模型训练包含两步——先训练/验证 GPR 高斯过程代理模型，
+                再将其知识蒸馏为轻量 NN（MLP）用于后续实时控制仿真。</div>""")
+            with gr.Tabs():
+                # ----- 子标签页 A: GPR 模型训练 -----
+                with gr.TabItem("📈 GPR 模型训练", id="gpr_sub_tab"):
                     with gr.Row():
                         with gr.Column(scale=1):
                             with gr.Group():
-                gr.HTML("<div class='card-title'>🧪 算例参数设置</div>")
+                                gr.HTML("<div class='card-title'>🔬 GPR 训练设置</div>")
+                                ui_dict["gpr_mode"] = gr.Radio(
+                                    choices=["load", "train"], value="load",
+                                    label="运行模式",
+                                    info="load: 加载已有权重 | train: 从头训练（需 botorch）")
+                                ui_dict["gpr_run_button"] = gr.Button(
+                                    "🚀 运行 GPR 训练 / 加载", variant="primary",
+                                    elem_classes="primary-btn")
+                            with gr.Group():
+                                gr.HTML("<div class='card-title'>📝 GPR 结果</div>")
+                                ui_dict["gpr_summary"] = gr.Markdown()
+                        with gr.Column(scale=2):
+                            ui_dict["gpr_plot"] = gr.Plot(label="GPR 模型结果")
+                            gr.HTML(f"""
+                            <div id="gpr-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
+                                {GPR_KNOWLEDGE}
+                            </div>
+                            {_mathjax_script('gpr-knowledge')}
+                            """)
+
+                # ----- 子标签页 B: NN 模型训练 -----
+                with gr.TabItem("🧠 NN 模型训练", id="nn_sub_tab"):
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            with gr.Group():
+                                gr.HTML("<div class='card-title'>🧪 NN 训练参数</div>")
+                                ui_dict["distill_epochs"] = gr.Slider(minimum=500, maximum=8000, value=3000, step=100,
+                                                                       label="训练轮数 (Epochs)", info="越多越精确，但耗时更长")
+                                ui_dict["distill_lr"] = gr.Slider(minimum=1e-4, maximum=1e-2, value=3e-3, step=1e-4,
+                                                                   label="学习率 (LR)", info="推荐 1e-3 ~ 5e-3")
+                                ui_dict["distill_hidden"] = gr.Slider(minimum=16, maximum=256, value=64, step=16,
+                                                                       label="隐藏层宽度", info="MLP每层神经元数")
+                                ui_dict["distill_run_button"] = gr.Button("🚀 开始 NN 训练", variant="primary",
+                                                                           elem_classes="primary-btn")
+                            with gr.Group():
+                                gr.HTML("<div class='card-title'>📝 NN 训练结果</div>")
+                                ui_dict["distill_summary"] = gr.Markdown()
+                        with gr.Column(scale=2):
+                            ui_dict["distill_plot"] = gr.Plot(label="NN 训练结果 (Loss + Parity)")
+                            gr.HTML(f"""
+                            <div id="nn-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
+                                {NN_KNOWLEDGE}
+                            </div>
+                            {_mathjax_script('nn-knowledge')}
+                            """)
+
+        # ========== 阶段一：发动机控制器设计 ==========
+        with gr.TabItem("🔧 发动机控制器设计", id="engine_tab"):
+            gr.HTML("""<div style='background:linear-gradient(135deg,#fff3e0,#ffe0b2);padding:10px 16px;
+                        border-radius:8px;margin-bottom:10px;font-size:0.92em;color:#e65100;'>
+                <b>阶段一</b>：选择 PID 或 MPC 控制器，调整参数，运行阶跃响应测试，观察功率跟踪性能。</div>""")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>🎯 控制器选择</div>")
+                        ui_dict["eng_controller_type"] = gr.Radio(
+                            choices=["PID", "MPC"], value="PID", label="控制器类型",
+                            info="PID: 经典三参数 | MPC: 模型预测控制")
+                    with gr.Group(visible=True) as eng_pid_group:
+                        gr.HTML("<div class='card-title'>🎛️ PID 参数</div>")
+                        ui_dict["eng_kp"] = gr.Slider(minimum=0.1, maximum=20, value=4.652, step=0.01,
+                                                       label="比例增益 Kp", info="增大加快响应，过大导致振荡")
+                        ui_dict["eng_ki"] = gr.Slider(minimum=0.0, maximum=20, value=7.078, step=0.01,
+                                                       label="积分增益 Ki", info="消除稳态误差，过大导致超调")
+                        ui_dict["eng_kd"] = gr.Slider(minimum=0.0, maximum=5, value=0.222, step=0.001,
+                                                       label="微分增益 Kd", info="抑制振荡，改善动态特性")
+                    ui_dict["eng_pid_group"] = eng_pid_group
+                    with gr.Group(visible=False) as eng_mpc_group:
+                        gr.HTML("<div class='card-title'>🎛️ MPC 参数</div>")
+                        ui_dict["eng_mpc_horizon"] = gr.Slider(minimum=3, maximum=30, value=15, step=1,
+                                                                label="预测时域 (Horizon)", info="MPC前看步数")
+                        ui_dict["eng_mpc_W_power"] = gr.Slider(minimum=1, maximum=500, value=200, step=1,
+                                                                label="功率跟踪权重 W_power")
+                        ui_dict["eng_mpc_W_dcost"] = gr.Slider(minimum=0.01, maximum=20, value=1.5, step=0.01,
+                                                                label="控制增量权重 W_Δcost")
+                        ui_dict["eng_mpc_overshoot"] = gr.Slider(minimum=1, maximum=30, value=5, step=1,
+                                                                  label="超调限制 (%)")
+                    ui_dict["eng_mpc_group"] = eng_mpc_group
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>⚙️ 发动机模型参数</div>")
+                        ui_dict["eng_tau_fuel"] = gr.Slider(minimum=0.05, maximum=1.0, value=0.15, step=0.01,
+                                                             label="燃油执行机构时间常数 τ (s)")
+                        ui_dict["eng_K_inertia"] = gr.Slider(minimum=10, maximum=500, value=100, step=5,
+                                                              label="转子惯性增益 K")
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>🧪 仿真设置</div>")
+                        ui_dict["eng_sim_time"] = gr.Slider(minimum=5, maximum=60, value=30, step=1,
+                                                             label="仿真时长 (s)")
+                        ui_dict["eng_dt"] = gr.Dropdown(choices=[0.02, 0.05, 0.1], value=0.02,
+                                                         label="仿真步长 (s)")
+                        ui_dict["eng_init_power"] = gr.Slider(minimum=20, maximum=250, value=100, step=5,
+                                                               label="初始功率 (kW)")
+                        ui_dict["eng_target_power"] = gr.Slider(minimum=20, maximum=300, value=200, step=5,
+                                                                 label="目标功率 (kW)")
+                        ui_dict["eng_run_button"] = gr.Button("🚀 运行发动机仿真", variant="primary",
+                                                               elem_classes="primary-btn")
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>📝 设计结果</div>")
+                        ui_dict["eng_summary"] = gr.Markdown()
+                with gr.Column(scale=2):
+                    ui_dict["eng_plot"] = gr.Plot(label="发动机控制器阶跃响应")
+                    gr.HTML(f"""
+                    <div id="engine-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
+                        {ENGINE_CONTROL_KNOWLEDGE}
+                    </div>
+                    {_mathjax_script('engine-knowledge')}
+                    """)
+
+            # Radio toggle PID/MPC visibility
+            ui_dict["eng_controller_type"].change(
+                fn=lambda ct: (gr.update(visible=(ct == "PID")), gr.update(visible=(ct == "MPC"))),
+                inputs=[ui_dict["eng_controller_type"]],
+                outputs=[eng_pid_group, eng_mpc_group],
+            )
+
+        # ========== 阶段二：电机控制器设计 ==========
+        with gr.TabItem("⚡ 电机控制器设计", id="motor_tab"):
+            gr.HTML("""<div style='background:linear-gradient(135deg,#e3f2fd,#bbdefb);padding:10px 16px;
+                        border-radius:8px;margin-bottom:10px;font-size:0.92em;color:#1565c0;'>
+                <b>阶段二</b>：选择 PID 或 MPC 控制器，运行转速跟踪 + 负载扰动测试。
+                在仿真60%时刻自动施加50%负载扰动，检验抗扰能力。</div>""")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>🎯 控制器选择</div>")
+                        ui_dict["mot_controller_type"] = gr.Radio(
+                            choices=["PID", "MPC"], value="PID", label="控制器类型",
+                            info="PID: 经典三参数 | MPC: 模型预测控制")
+                    with gr.Group(visible=True) as mot_pid_group:
+                        gr.HTML("<div class='card-title'>🎛️ PID 参数</div>")
+                        ui_dict["mot_kp"] = gr.Slider(minimum=0.1, maximum=50, value=5.0, step=0.1,
+                                                       label="比例增益 Kp", info="增大加快转速响应")
+                        ui_dict["mot_ki"] = gr.Slider(minimum=0.0, maximum=100, value=2.0, step=0.1,
+                                                       label="积分增益 Ki", info="消除转速稳态偏差")
+                        ui_dict["mot_kd"] = gr.Slider(minimum=0.0, maximum=5, value=0.5, step=0.01,
+                                                       label="微分增益 Kd", info="抑制转速振荡")
+                    ui_dict["mot_pid_group"] = mot_pid_group
+                    with gr.Group(visible=False) as mot_mpc_group:
+                        gr.HTML("<div class='card-title'>🎛️ MPC 参数</div>")
+                        ui_dict["mot_mpc_W_speed"] = gr.Slider(minimum=1, maximum=500, value=200, step=1,
+                                                                label="转速跟踪权重 W_speed")
+                        ui_dict["mot_mpc_W_dcost"] = gr.Slider(minimum=0.01, maximum=20, value=0.3, step=0.01,
+                                                                label="控制增量权重 W_Δcost")
+                        ui_dict["mot_mpc_overshoot"] = gr.Slider(minimum=1, maximum=30, value=5, step=1,
+                                                                  label="超调限制 (%)")
+                    ui_dict["mot_mpc_group"] = mot_mpc_group
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>⚙️ 电机模型参数</div>")
+                        ui_dict["mot_J"] = gr.Slider(minimum=0.1, maximum=5.0, value=1.0, step=0.1,
+                                                      label="转动惯量 J (kg·m²)", info="越大响应越慢但越平稳")
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>🧪 仿真设置</div>")
+                        ui_dict["mot_sim_time"] = gr.Slider(minimum=5, maximum=60, value=30, step=1,
+                                                             label="仿真时长 (s)")
+                        ui_dict["mot_dt"] = gr.Dropdown(choices=[0.02, 0.05, 0.1], value=0.02,
+                                                         label="仿真步长 (s)")
+                        ui_dict["mot_target_rpm"] = gr.Slider(minimum=500, maximum=5000, value=2000, step=50,
+                                                               label="目标转速 (RPM)")
+                        ui_dict["mot_load_torque"] = gr.Slider(minimum=10, maximum=400, value=80, step=5,
+                                                                label="负载转矩 (Nm)")
+                        ui_dict["mot_run_button"] = gr.Button("🚀 运行电机仿真", variant="primary",
+                                                               elem_classes="primary-btn")
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>📝 设计结果</div>")
+                        ui_dict["mot_summary"] = gr.Markdown()
+                with gr.Column(scale=2):
+                    ui_dict["mot_plot"] = gr.Plot(label="电机控制器阶跃响应")
+                    gr.HTML(f"""
+                    <div id="motor-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
+                        {MOTOR_CONTROL_KNOWLEDGE}
+                    </div>
+                    {_mathjax_script('motor-knowledge')}
+                    """)
+
+            # Radio toggle PID/MPC visibility
+            ui_dict["mot_controller_type"].change(
+                fn=lambda ct: (gr.update(visible=(ct == "PID")), gr.update(visible=(ct == "MPC"))),
+                inputs=[ui_dict["mot_controller_type"]],
+                outputs=[mot_pid_group, mot_mpc_group],
+            )
+
+        # ========== 阶段三：能量管理策略设计 ==========
+        with gr.TabItem("🔋 能量管理策略设计", id="ems_tab"):
+            gr.HTML("""<div style='background:linear-gradient(135deg,#e8f5e9,#c8e6c9);padding:10px 16px;
+                        border-radius:8px;margin-bottom:10px;font-size:0.92em;color:#2e7d32;'>
+                <b>阶段三</b>：设计基于规则的能量管理策略（自动引用前两阶段的控制器参数）。<br>
+                <b>策略原理</b>：SOC &lt; 下限阈值 → 进入<b>充电模式</b>;
+                SOC &gt; 上限阈值 → 退出充电，进入<b>功率跟随模式</b>。
+                下限~上限之间为<b>滞环区间</b>，防止模式频繁切换。</div>""")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>📊 SOC规则参数（滞环控制）</div>")
+                        ui_dict["soc_target"] = gr.Slider(minimum=30, maximum=80, value=60, step=1,
+                                                           label="SOC目标值 (%)", info="功率跟随模式下的SOC补偿基准")
+                        ui_dict["soc_low"] = gr.Slider(minimum=15, maximum=50, value=30, step=1,
+                                                        label="SOC下限阈值 (%)", info="低于此值→进入充电模式")
+                        ui_dict["soc_high"] = gr.Slider(minimum=50, maximum=90, value=70, step=1,
+                                                         label="SOC上限阈值 (%)", info="高于此值→退出充电模式")
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>⚡ 功率规则参数</div>")
+                        ui_dict["p_eng_min"] = gr.Slider(minimum=10, maximum=100, value=20, step=5,
+                                                          label="发动机最小功率 (kW)")
+                        ui_dict["p_eng_max"] = gr.Slider(minimum=100, maximum=350, value=300, step=10,
+                                                          label="发动机最大功率 (kW)")
+                        ui_dict["p_charge"] = gr.Slider(minimum=50, maximum=300, value=200, step=10,
+                                                         label="充电模式发动机功率 (kW)", info="进入充电模式后发动机固定输出")
+                        ui_dict["k_soc"] = gr.Slider(minimum=0, maximum=200, value=50, step=5,
+                                                      label="SOC补偿增益 (kW/ΔSOC)", info="跟随模式下对SOC偏差的修正力度")
+                        ui_dict["power_reserve"] = gr.Slider(minimum=0, maximum=50, value=10, step=1,
+                                                              label="动态功率储备 (%)", info="发动机额外预留功率百分比")
+                    with gr.Group():
+                        gr.HTML("<div class='card-title'>🔧 系统与仿真参数</div>")
+                        ui_dict["battery_capacity"] = gr.Slider(minimum=10, maximum=200, value=50, step=5,
+                                                                 label="电池容量 (kWh)")
+                        ui_dict["initial_soc"] = gr.Slider(minimum=10, maximum=95, value=60, step=1,
+                                                            label="初始SOC (%)")
+                        ui_dict["initial_engine_power"] = gr.Slider(minimum=20, maximum=250, value=50, step=5,
+                                                                     label="初始发动机功率 (kW)")
                         ui_dict["profile"] = gr.Dropdown(
                             choices=["起飞-巡航-降落", "高机动阶跃", "经济巡航"],
-                    value="起飞-巡航-降落",
+                            value="起飞-巡航-降落", label="工况模板")
-                    label="工况模板"
+                        ui_dict["sim_time"] = gr.Slider(minimum=20, maximum=180, value=60, step=5,
-                )
+                                                         label="仿真时长 (s)")
-                ui_dict["sim_time"] = gr.Slider(minimum=20, maximum=180, value=60, step=5, label="仿真时长 (s)")
+                        ui_dict["dt"] = gr.Dropdown(choices=[0.02, 0.05, 0.1], value=0.02,
-                ui_dict["dt"] = gr.Dropdown(choices=[0.02, 0.05, 0.1], value=0.02, label="仿真步长 (s)")
+                                                     label="仿真步长 (s)")
-                ui_dict["initial_soc"] = gr.Slider(minimum=20, maximum=90, value=60, step=1, label="初始SOC (%)")
+                        ui_dict["hybrid_run_button"] = gr.Button("🚀 运行混动系统仿真", variant="primary",
-                ui_dict["initial_engine_power"] = gr.Slider(minimum=20, maximum=250, value=50, step=5, label="初始发动机功率 (kW)")
+                                                                  size="lg", elem_classes="primary-btn")
-                ui_dict["rpm_scale"] = gr.Slider(minimum=0.6, maximum=1.4, value=1.0, step=0.05, label="目标转速缩放系数")
-                ui_dict["load_scale"] = gr.Slider(minimum=0.6, maximum=1.4, value=1.0, step=0.05, label="负载转矩缩放系数")
-                ui_dict["run_button"] = gr.Button("🚀 运行混动算例", variant="primary", elem_classes="primary-btn")
                     with gr.Group():
-                gr.HTML("<div class='card-title'>📝 结果解读</div>")
+                        gr.HTML("<div class='card-title'>📝 结果摘要</div>")
-                ui_dict["summary"] = gr.Markdown()
+                        ui_dict["hybrid_summary"] = gr.Markdown()
                 with gr.Column(scale=2):
-            ui_dict["plot"] = gr.Plot(label="混动系统响应图")
+                    ui_dict["hybrid_plot"] = gr.Plot(label="混动系统响应图")
-            ui_dict["table"] = gr.Dataframe(
+                    ui_dict["hybrid_table"] = gr.Dataframe(
-                headers=["时间(s)", "目标转速", "实际转速", "发动机功率(kW)", "电池功率(kW)", "SOC(%)"],
+                        headers=["时间(s)", "目标转速", "实际转速", "发动机功率(kW)",
-                label="关键时刻数据",
+                                 "电池功率(kW)", "SOC(%)", "EMS模式"],
-                interactive=False
+                        label="关键时刻数据", interactive=False
                     )
+                    gr.HTML(f"""
+                    <div id="ems-knowledge" style="max-height:600px;overflow-y:auto;padding-right:8px;">
+                        {EMS_KNOWLEDGE}
+                    </div>
+                    {_mathjax_script('ems-knowledge')}
+                    """)
     return ui_dict
 
 def create_chatbot_tab():
@@ -260,3 +562,4 @@ def create_chatbot_tab():
         label="💡 试试这些问题："
     )
     return ui_dict
+