基于学习的无人驾驶车辆模型预测路径跟踪控制研究

For the trade-off between prediction model accuracy and computational cost for path tracking control of autonomous vehicles，a learning-based model predictive control（LB-MPC）path tracking control strategy is proposed in this paper. A two-degree-of-freedom single-track vehicle dynamic model is established，and an in-depth analysis is conducted on its step response error with respect to variation in vehicle speed，pedal position，and front wheel steering angle compared to the IPG TruckMaker model.Methods for constructing error datasets and re⁃ ceding horizon updates are designed，and the 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 pro⁃ gramming optimization problem，proposing a learning-based model predictive path tracking control architecture. Through joint simulation using the IPG TruckMaker & Simulink platform and real vehicle experiments，the real-time performance and effectiveness of the proposed GPR error correction model and LB-MPC path tracking control strategy are verified. The results show that compared to the traditional model predictive control（MPC）path tracking control strategy，the proposed LB-MPC strategy reduces the average path tracking error by 23.64%.