Path Tracking Control of Unmanned Vehicle Based on Adaptive Steering Angle Constraint

The research and application of nonlinear model predictive control (NMPC) in the path tracking of unmanned vehicles are gradually increasing. However, the consideration of the relationship between the constrained values of steering angle and the vehicle speed on the handling stability and ride comfort is insufficient in the current research of path tracking control. In order to improve the applicability of NMPC in path tracking, this paper analyzes the constrained values of steering angle and vehicle speed on handling stability and tracking error in lane change scenario with traditional NMPC algorithm, and then fuzzy control is used to divide the maximum steering angle of front wheel at different speeds which can improve the tracking performance of traditional NMPC controller. Finally, the handling stability and tracking performance in path tracking are simulated and verified. The simulation results show that the improved NMPC controller can achieve the tracking control of vehicles at different speeds on the premise of ensuring ride comfort and tracking accuracy.