Path Tracking Control of Intelligent Vehicle Based on Learning Model Predictive Control

Abstract

Path tracking control is a key technology for intelligent vehicles. However, the existing vehicle tracking control methods mostly rely on more accurate vehicle control models, while actual vehicle control systems mostly have modeling errors, parameter perturbations and external disturbances, which significantly affect path tracking control accuracy. In this paper, a learning path tracking control method for intelligent vehicles considering unmodeled dynamics of vehicles is proposed. Firstly, a nominal model of the vehicle is established and a linear prediction model is used to approximate the compensation for the unmodeled dynamics of the vehicle to improve the accuracy of the vehicle model. Then, learning and updating of the parameters of the unmodeled dynamics are realized based on the principle of Extended Kalman Filtering. Next, learning Model Predictive Controller (LMPC) considering the unmodeled dynamics of the system is established. Finally, the effectiveness of the proposed method in improving the path tracking accuracy is verified by designing a joint simulation test with Carsim and Matlab/Simulink for multiple operating conditions and multiple groups.