基于模型预测控制与离散线性二次型调节器的智能车横纵解耦跟踪控制

Horizontal and Vertical Decoupling Tracking Control Based on MPC and DLQR

为确保智能车在复杂曲率变化道路条件下的跟踪精度与横向稳定性,提出一种基于Frenet坐标系的横纵解耦跟踪控制方法,并通过模糊速度规划提升跟踪的可靠性。横向控制采用基于前馈补偿的离散线性二次型调节器(DLQR)使跟踪误差收敛,纵向控制采用模型预测控制(MPC)输出期望加速度并结合油门制动标定表实现速度跟踪。速度规划方面以横向跟踪误差与道路曲率作为输入信号进行模糊速度规划。基于CarSim/Simulink构建仿真模型进行仿真验证,结果表明:该控制方式可有效降低路径跟踪误差,提高车辆横向稳定性以满足不同道路曲率的行车工况。

To ensure the tracking accuracy and lateral stability of intelligent vehicles under complex road conditions with curvature change,this paper proposed a horizontal and vertical decoupling tracking control based on Frenet coordinate system,and improved the tracking reliability through fuzzy speed planning.Discrete Linear Quadratic Regulator(DLQR)was used for horizontal control based on feedforward compensation to make the tracking error converge,and Model Prediction Control(MPC)was used for longitudinal control to output the desired acceleration and combined with the accelerator brake calibration meter to achieve speed tracking.In terms of speed planning,lateral tracking error and road curvature are used as input signals for fuzzy speed planning.A simulation model was built based on CarSim/Simulink for simulation and verification,the results show that the control method can effectively reduce the path tracking error and improve the lateral stability of the vehicle to meet the driving conditions of different road curvature.