郑州大学学报（工学版）2024，Vol.45Issue(1)：47-53,7.DOI:10.13705/j.issn.1671-6833.2023.04.005
自适应时域参数MPC的智能车辆轨迹跟踪控制
Intelligent Vehicle Trajectory Tracking Control Based on Adaptive Time Domain Parameter MPC
摘要
Abstract
In order to solve the problem of stability and control accuracy of intelligent vehicle active steering track-ing control on low adhesion road surface,an intelligent vehicle trajectory tracking control strategy based on adaptive time domain parameters was proposed.Based on the vehicle dynamics model and model predictive control algorithm(MPC),a linear time-varying MPC controller was established,and dynamic constraints including tire side deflec-tion constraints,centroid side deflection constraints and front wheel angle constraints were added to solve the opti-mal front wheel steering angle.The influence of time domain parameters in the controller on the control effect was analyzed,and an adaptive time domain parameter controller was designed.According to the acquired vehicle speed,the optimal predictive time domain and control time domain parameters were obtained and input to the con-troller,improving the control accuracy and stability of the controller at different speeds.By building the MATLAB/SimuLink and CarSim co-simulation platform,the fixed time domain controller and adaptive time domain controller were compared and simulated with the condition of low adhesion road surface.The results showed that the adaptive time-domain controller could effectively improve the performance of the controller,reduce the lateral deviation,and improve the control accuracy of trajectory tracking.At the same time,it also had strong adaptability to different speeds,and the lateral deflection angle of the vehicle center of mass was controlled within 0°-1.5°,which effec-tively ensured the stability of the vehicle.关键词
智能车辆/轨迹跟踪/模型预测控制/自适应/前轮主动转向Key words
intelligent vehicle/trajectory tracking/model predictive control/adaptive/active front steering分类
交通工程引用本文复制引用
刘志强,张晴..自适应时域参数MPC的智能车辆轨迹跟踪控制[J].郑州大学学报（工学版）,2024,45(1):47-53,7.基金项目
国家自然科学基金资助项目(72001095) （72001095）
