Path Tracking Robust Control Strategy for Intelligent Vehicle Based on Force-Driven with MPC and H∞

Abstract Due to errors in vehicle dynamics modeling, uncertainty in model parameters, and disturbances from curvature, the performance of the path tracking controller is poor or even unstable under high-speed and large-curvature conditions. Therefore, a path tracking robust control strategy based on force-driven H ∞ and MPC is proposed. To fully exploit the nonlinear dynamics characteristics of tires, a force-driven state space model of a path tracking system based on a linear time-varying tire model is established; the H ∞ and MPC methods are used to design a robust controller. Considering disturbance and system state constraints, the robust control constraint model based on LMI is established. Finally, the proposed controller is validated through joint simulations using CarSim and MATLAB. The results show that the maximum lateral deviation is reduced by 17.07%, and the maximum course angle deviation is reduced by 13.04% under large curvature disturbance conditions. The maximum lateral deviation is reduced by 27.85%, and the maximum course angle deviation is reduced by 31.17% under conditions of uncertain road adhesion coefficients. Based on the controller's performance, the proposed controller effectively mitigates modeling errors, parameter uncertainties, and curvature disturbances.