Human–Machine Shared Control for Steer-by-Wire Vehicles Using Improved Reinforcement Learning-Based MPC

To enhance the trajectory tracking capability of steer-by-wire (SBW) vehicles while reducing driver’s workload, a human-machine shared control (HMSC) strategy using improved reinforcement learning-based model predictive control (RL-MPC) is proposed. In this paper, two main contributions have been made: 1) for driver loop, a variable steering ratio (VSR) strategy applied to SBW system is designed based on an improved fuzzy controller, whose parameters are optimized through simulated annealing (SA) algorithm; 2) for intelligent control system loop, an improved RL-MPC method is proposed to realize the high precision steering tracking control for autonomous vehicles (AVs), in which MPC and deep deterministic policy gradient (DDPG) are deeply integrated to combine their short-term optimization ability and long-term value estimation capability. Moreover, to shorten the time of overall training and ensure that the optimal control strategy can be explored, the DDPG agent is pretrained before the parallel training of RL-MPC. CarSim-MATLAB/Simulink co-simulation results show that in the whole tracking process, the lateral position error and yaw angle error of the vehicle are significantly reduced, indicating that the tracking accuracy is greatly improved. Meanwhile, the steering wheel angle and speed are also reduced, which means that the driver will spend less energy during the steering process.