Learning Based Model Predictive Path Tracking Control for Autonomous Buses

In addressing the trade-off between prediction model accuracy and computational cost in the context of path tracking control, this paper proposes a learning-based model predictive control (LB-MPC) strategy for autonomous buses. A three-degree-of-freedom (DOF) single-track vehicle dynamic model is established, and an in-depth analysis is conducted on its step response error with respect to variations in vehicle speed, pedal position, and front wheel steering angle compared to the IPG TruckMaker model. Methods for constructing error datasets and receding horizon updates are designed, and a Gaussian process regression (GPR) is employed to establish an error fitting model for real-time error compensation and correction of the nominal single-track model. The error correction model is utilized as the prediction model, and a path tracking cost function is designed to formulate a quadratic programming (QP) optimization problem, proposing an LB-MPC path tracking control architecture. Through joint simulations using the IPG TruckMaker & Simulink platform and real bus experiment, the real-time performance and effectiveness of the proposed GPR error correction model and LB-MPC path tracking control strategy are verified. Results demonstrate that compared to traditional MPC path tracking control strategy, the proposed LB-MPC strategy reduces the average path tracking error by 79.00%.