中国机械工程2026，Vol.37Issue(8)：1900-1908,9.DOI:10.3969/j.issn.1004-132X.2026.08.009
基于物理-学习混合动力学建模的智能车辆横向控制研究
Research on Lateral Control of Intelligent Vehicles Based on Physics-Learning Hybrid Dynamics Modeling
摘要
Abstract
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.关键词
智能车辆/混合模型/模型预测控制/横向控制Key words
intelligent vehicle/hybrid model/model predictive control(MPC)/lateral control分类
交通工程引用本文复制引用
时培成,孙雨辰,CHAKIR Chadia,孙羽..基于物理-学习混合动力学建模的智能车辆横向控制研究[J].中国机械工程,2026,37(8):1900-1908,9.基金项目
中央引导地方科技专项-长三角科技创新共同体联合攻关计划(2023CSJGG1600) （2023CSJGG1600）
安徽省自然科学基金(2208085MF173) （2208085MF173）
芜湖市"赤铸之光"重大科技项目(2023zd01,2023zd03) （2023zd01,2023zd03）
