基于物理-学习混合动力学建模的智能车辆横向控制研究OA

To address the insufficient accuracy of traditional dynamic models for intelligent vehicle lat-eral control under complex operating conditions,this paper proposes a physics-based learning hybrid dy-namic modeling method combined with learning-based MPC to enhance lateral control precision and robust-ness.A single-track model is first adopted as the foundational physical model and augmented with a gated recurrent unit(GRU)network to compensate for unmodeled dynamics,thereby constructing a hybrid model that integrates physical interpretability with data-driven adaptability.The hybrid model is then em-bedded as the predictive model within an MPC framework,in which an optimization objective function in-corporating trajectory tracking errors and control input constraints is designed to compute optimal front-wheel steering angle commands in real time.CarSim/Simulink co-simulation results under double-lane-change and serpentine maneuvers demonstrate that the proposed method reduces both the root-mean-square lateral tracking error and the front-wheel steering angle amplitude compared with benchmark meth-ods,while effectively suppressing unmodeled dynamics inherent in the single-track model.The proposed approach achieves precise control output under complex conditions,improving both lateral control accuracy and stability,and thus demonstrates superior lateral control performance for intelligent vehicles.