Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles

Abstract

This paper proposes an adaptive path tracking control method tailored for intelligent vehicles, aiming to enhance accuracy and stability. Initially, based on the traditional model predictive control (MPC) theory, the lateral speed stability boundary concerning the vehicle yaw rate is derived to establish the constraint conditions. Subsequently, optimal time domain parameters are determined across 100 typical curve conditions using a genetic algorithm. To achieve condition-adaptive path tracking control, speed and road curvature feedback are integrated into the MPC controller, enabling real-time adjustment of optimal control parameters. The simulation results from CarSim and Simulink co-simulation, as well as hardware-in-the-loop (HIL) experiments, demonstrate that the proposed method significantly improves path tracking accuracy for intelligent vehicles under varying curvature path conditions, outperforming both traditional MPC and higher-order sliding mode control (HOSMC) controllers.