Variable-parameter MPC Multi-objective Control for Intelligent Vehicle Path Tracking Based on Reinforcement Learning
To address the problems of tracking accuracy degradation and stability deterioration when operating intelligent vehicles under changing driving conditions,a multi-objective control strategy based on reinforcement learning variable parameter model predictive control(MPC)algorithm was proposed in this study.The proposed method effectively realizes the parameter adaptive tuning of intelligent vehicle path tracking control system.The proposed linear time-varying MPC controller was designed based on a vehicle dynamics model to obtain the optimal front-wheel steering angle and additional yaw moment.Based on the Actor-Critic reinforcement learning architecture,the Deep Deterministic Policy Gradient(DDPG)and Twin Delayed Deep Deterministic Policy Gradient(TD3)agents were designed for control parameter tuning.The gain function was constructed with tracking accuracy and system stability as the goal,and two typical simulation scenarios of docking road and variable curvature road were constructed for the algorithm performance verification.For the docking road scenario,the prediction horizon and weight matrix of the controller were adjusted in time according to the changes in the road adhesion coefficient.Whereas for the variable curvature road scenario,the prediction horizon and weight matrix of the controller were adjusted in time according to the changes in the road curvature.Through joint simulation analyses conducted using MATLAB/SimuLink,CarSim,and Python,the reinforcement learning-tuned MPC was compared with fixed parameter MPC and Fuzzy-tuned MPC models.The results showed that the reinforcement learning methods yielded the best performance regarding the path tracking accuracy of intelligent vehicles under different road conditions,while guaranteeing the vehicle safety as much as possible.Under the docking road condition,compared with the fixed parameter MPC and Fuzzy-tuned MPC models,the average lateral deviation of the vehicle was reduced by 99.8％and 97.6％,respectively,when using the reinforcement learning-tuned MPC,and the average front-wheel angle change rate was reduced by 99.7％and 77.0％,respectively.Moreover,under the variable curvature road condition,the average lateral deviation was decreased by 79.6％and 90.8％,respectively,and the average front-wheel angle change rate decreased by 40.6％and 2.6％,respectively,compared with those obtained when using the fixed parameter MPC and Fuzzy-tuned MPC models.
automotive engineeringpath trackingmodel predictive controlreinforcement learn-ingcontrol parameter tuningadditional yaw moment control
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