A Longitudinal/Lateral Coupled Neural Network Model Predictive Controller for Path Tracking of Self-Driving Vehicle

In recent years, the model predictive control (MPC) algorithm has been increasingly applied to the path tracking of self-driving vehicles due to its capacity to deal with dynamic constraints explicitly. The control performance of MPC is highly dependent on the accuracy dynamic model; however, as vehicles are strongly coupled nonlinear systems, the prediction accuracy of the classical mechanism model decreases significantly at high-speed conditions, leading to increased control errors. This paper proposes replacing the classical mechanism model with a recurrent neural network (RNN) for vehicle dynamical state prediction under the framework of MPC to achieve higher control effects under high speed steering processes. The RNN vehicle dynamic model uses historical data of control and state variables to predict future states. Based on this novel model, longitudinal/lateral coupled model predictive control is realized. The differential evolution algorithm is proposed to solve the optimization problem in the controller. Finally, the prediction accuracy of the RNN model is verified on the real vehicle dataset and compared with linear/nonlinear mechanism models. The control algorithm proposed in this paper is compared with classical MPC against low and high speeds (10m/s and 30m/s) on the ADAMS/Python/Simulink joint simulation platform. The results show that the control accuracy and stability of the longitudinal/lateral coupled neural network MPC are higher than classical MPC, especially at high speed.