摘要
为了应对自动驾驶交通环境的复杂性以及不同车速下路径跟踪误差增大的问题,提出了一种基于模型预测控制(MPC)的自适应路径跟踪控制器。该控制器以车辆实时速度和横向位移误差为双输入变量,通过模糊控制方法选择当前最优的预测时域和控制时域,从而设计出自适应时域路径跟踪控制策略。通过CarSim与MATLAB/Simulink的联合仿真结果表明,车辆最大横向误差降低32%,最大横摆角速度降低11%。改进后的路径跟踪控制器在变速行驶条件下,能够实现良好的路径跟踪精度和系统稳定性。
To address the complexity of autonomous driving in varying traffic environments and the increase in path tracking errors at different speeds,an adaptive path tracking controller based on model predictive control(MPC)was proposed.This controller utilizes real-time vehicle speed and lateral displacement error as dual input variables,employing a fuzzy control method to select the optimal prediction horizon and control horizon.This leads to the design of an adaptive horizon path tracking control strategy.Joint simulations using CarSim and MATLAB∕Simulink showed that the maximum lateral error was reduced by 32%and the maximum yaw rate was reduced by 11%.The improved path tracking controller achieves good path tracking accuracy and system stability under varying speed conditions.
作者
赵杰
杨振跃
李晋
ZHAO Jie;YANG Zhenyue;LI Jin(School of Electrical and Control Engineering,Heilongjiang University of Science and Technology,Harbin150022,Heilongjiang,China)
出处
《农业装备与车辆工程》
2024年第11期44-47,57,共5页
Agricultural Equipment & Vehicle Engineering
基金
黑龙江省省属高校基本科研业务费项(2022-KYYWF-0551)。
关键词
智能汽车
路径跟踪
模型预测控制
自适应
intelligent vehicle
path tracking
model predictive control
adaptive
