Model predictive path tracking control of intelligent vehicle based on dual-stage disturbance observer under multi-channel disturbances

Parameter fluctuations, unmodeled dynamics, speed variation, steering actuator faults, and other multi-channel uncertain disturbances are the key challenges faced by the path tracking control of intelligent vehicles, which will affect the accuracy and stability of the path tracking. Therefore, a model predictive control (MPC) method based on a dual-stage disturbance observer (DDOB) is proposed in this paper. First, a tracking error dynamics model considering multi-channel uncertain disturbances is constructed, based on which a model predictive controller is designed to obtain the nominal front wheel steering angle by the Karush-Kuhn-Tucker (KKT) condition. Furthermore, the dual-stage disturbance observer is designed to enable real-time estimation of the system disturbances, and then the estimated disturbances are used as the compensation for the nominal front wheel steering angle, which establishes the MPC control law with parallel compensation of the dual-stage disturbance observer. Finally, the error boundedness of the dual-stage disturbance observer and the global stability of the model predictive controller are analyzed. The effectiveness and superiority of the proposed algorithm are verified through Carsim-Simulink simulation and hardware-in-the-loop (HiL) experiments.