基于自适应模型预测控制的智能车辆路径跟踪研究  

Research on Vehicle Path Tracking Based onAdaptive Model Predictive Control

作　　者：阎明涛 符朝兴[1] YAN Mingtao;FU Chaoxing(College of Mechanical and Electrical Engineering,Qingdao University,Qingdao 266071,China)

机构地区：[1]青岛大学机电工程学院,山东青岛266071

出　　处：《青岛大学学报(工程技术版)》2024年第4期68-76,共9页Journal of Qingdao University(Engineering & Technology Edition)

摘　　要：为了提高智能车辆在高速行驶下的路径跟踪的控制精度和稳定性能,提出了基于自适应模型预测控制(Adaptive model predictive control, AMPC)路径跟踪算法。在CarSim仿真软件中选定车辆模型参数,建立仿真车辆动力学模型;以路径跟踪误差最小作为控制目标,建立状态空间方程,在Matlab的MPC Designer中建立了智能车辆的目标函数,设定约束条件,设计自适应模型预测控制器;基于路径跟踪误差自适应调整模型预测控制器参数,搭建Matlab/Simulink与CarSim联合仿真模型框架,完成不同车速下智能车辆双车道换道路径跟踪控制的仿真验证。仿真结果表明,在车速10 km/h、30 km/h下,AMPC的路径跟踪最大误差为0.11 m,最大误差降低10%。在车速60 km/h、90 km/h时,AMPC的路径跟踪最大误差为0.05 m。To enhance the control precision and stability of path tracking for intelligent vehicles at high speeds,an Adaptive Model Predictive Control(AMPC)algorithm was proposed.Vehicle model parameters were selected from CarSim simulation software to establish a dynamic model of the vehicle.By minimizing the path tracking error as the control objective,a state-space equation was formulated,and an objective function for intelligent vehicles was developed in MATLAB MPC Designer with set constraints to design an adaptive model predictive controller.This controller adjusted its parameters automatically based on path tracking error feedback,integrated a Matlab/Simulink and CarSim simulation model framework to verify the simulation of intelligent vehicle dual-lane change path tracking control at various speeds.Simulation results indicated that at speeds of 10 km/h and 30 km/h,the AMPC reduced the maximum path tracking error to 0.11 m,lowered the error rate by 10%.At higher speeds of 60 km/h and 90 km/h,the maximum error was maintained at 0.05 m.

关 键 词：自适应模型预测控制 路径跟踪 MATLAB CARSIM 联合仿真 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]