A Fast Predictive Control Method for Vehicle Path Tracking Based on a Recurrent Neural Network

Abstract

The paper introduces a Fast Model Predictive Control (FMPC) approach for vehicle path tracking, addressing the challenge of real-time performance in highly nonlinear systems. Utilizing a recurrent neural network with symmetric saturating linear transfer functions (SSL-RNN), our method efficiently constructs an SSL-RNN model for the vehicle. By transforming the MPC optimal control problem into a mixed integer linear programming problem, a swift online solution is achieved. Through simulations on a CarSim/Simulink platform, our FMPC outperforms RNN-based nonlinear MPC and long-short-term memory network-based MPC, demonstrating superior accuracy in vehicle path tracking and enhanced controller solution efficiency.

Keywords

• Mixed integer linear programming
• model predictive control
• recurrent neural network
• symmetric saturating linear transfer functions
• vehicle path tracking