Path Tracking Control of Autonomous Vehicle on Curved Road Considering Multi-Source Uncertainty

Aiming at the system multi-source uncertainty problem during the path tracking control of intelligent vehicle in complex curved road environments, the model predictive control algorithm based on the extended state observer is proposed. Firstly, based on the vehicle dynamics theory, intelligent vehicle path tracking error model is established that takes into account the uncertainty of vehicle parameters and the uncertainty of road curvature, road attachment conditions and other random interference factors, and an online random disturbance estimation method based on the extended state observer is proposed. At the same time, the whale optimization algorithm is used to optimize the relevant parameters of the expanded state observer. Then combined with interference estimation to establish intelligent vehicle path tracking accuracy and driving stability index functions and constraints, and design a path tracking model predictive control method based on the extended state observer. Next, the tracking control problem is transformed into an active steering optimal control problem under dynamic uncertainty interference. Finally, the Carsim-Simulink/Matlab co-simulation verifies the superiority of the algorithm proposed in this paper.