基于变预测时域MPC的自动驾驶汽车轨迹跟踪控制研究

doi:10.3901/JME.2022.24.275

机械工程学报 ›› 2022, Vol. 58 ›› Issue (24): 275-288.doi: 10.3901/JME.2022.24.275

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基于变预测时域MPC的自动驾驶汽车轨迹跟踪控制研究

杜荣华^1,2, 胡鸿飞¹, 高凯^1,2, 黄浩¹

1. 长沙理工大学汽车与机械工程学院长沙 410114;
2. 长沙理工大学智能道路与车路协同湖南省重点实验室长沙 410114

收稿日期:2022-01-25 修回日期:2022-09-20 出版日期:2022-12-20 发布日期:2023-04-03
通讯作者: 高凯(通信作者),男,1985年出生,博士,副教授,硕士研究生导师。主要研究方向为自动驾驶汽车感知与控制,智能交通与车联网应用。E-mail:kai_g@csust.edu.cn
作者简介:杜荣华,男,1973年出生,博士,教授,硕士研究生导师。主要研究方向为智能交通与车路协同技术,车路联网与车辆安全控制技术,交通信息化技术。E-mail:csdrh@163.com;高凯(通信作者),男,1985年出生,博士,副教授,硕士研究生导师。主要研究方向为自动驾驶汽车感知与控制,智能交通与车联网应用。E-mail:kai_g@csust.edu.cn
基金资助:
国家自然科学基金（61973047）和湖南省自然科学基金（2020JJ4603）资助项目。

Research on Trajectory Tracking Control of Autonomous Vehicle Based on MPC with Variable Predictive Horizon

DU Ronghua^1,2, HU Hongfei¹, GAO Kai^1,2, HUANG Hao¹

1. College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114;
2. Hunan Key Laboratory of Smart Roadwasy and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114

Received:2022-01-25 Revised:2022-09-20 Online:2022-12-20 Published:2023-04-03

摘要/Abstract

摘要： 为了保证自动驾驶汽车轨迹跟踪的精度及行驶过程中的稳定性，提出一种基于车辆横向稳定状态在线识别和模糊算法的变预测时域模型预测控制（MPC）方法。针对车辆稳定状态的在线识别，采用k-means聚类算法对车辆行驶状态参数进行聚类分析，得到聚类质心，通过在线对比当前车辆状态量与不同聚类质心之间的欧氏距离获取车辆的实时安全等级。同时计算出当前车辆的轨迹跟踪横向偏移量，以这二者为输入，通过模糊控制算法在线计算出预测时域的变化量并输出给MPC控制器实现预测时域的自适应调整，最后求解出自动驾驶车辆跟踪轨迹的最优的控制序列，以达到在保持车辆稳定的前提下实现高精度轨迹跟踪控制的目的。CarSim/Simulink联合仿真结果表明，改进后的变预测时域MPC算法在提高自动驾驶汽车轨迹跟踪精度及横向稳定性方面的表现优于传统MPC控制器。

关键词: 自动驾驶, 轨迹跟踪, k-means算法, 模糊算法, 模型预测控制

Abstract: In order to ensure the trajectory tracking accuracy and driving stability of autonomous vehicles, a model predictive control with variable predictive horizon method was proposed based on on-line identification of vehicle lateral stability state and fuzzy algorithm. Aiming at the online recognition of vehicle stable state, k-means clustering algorithm was used to cluster the parameters of vehicle driving state and obtain the cluster centroid. The real-time safety level of vehicle was obtained by comparing the Euclidean distance between the current vehicle state quantity and different cluster centroids online. At the same time, the lateral offset of the current vehicle track tracking is calculated. With the two as inputs, the variation of prediction time domain is calculated online by fuzzy algorithm and output to MPC controller to realize adaptive adjustment of prediction time domain. Finally, the optimal control sequence of the track tracking of the autonomous vehicle is solved to achieve the goal of high precision trajectory tracking control under the premise of maintaining vehicle stability. The results of CarSim/Simulink co-simulation show that the improved MPC algorithm is superior to the traditional MPC controller in improving the trajectory tracking accuracy and lateral stability of autonomous vehicles.

Key words: automatic drive, trajectory tracking, k-means algorithm, fuzzy algorithm, model predictive control

中图分类号:

TP242

杜荣华, 胡鸿飞, 高凯, 黄浩. 基于变预测时域MPC的自动驾驶汽车轨迹跟踪控制研究[J]. 机械工程学报, 2022, 58(24): 275-288.

DU Ronghua, HU Hongfei, GAO Kai, HUANG Hao. Research on Trajectory Tracking Control of Autonomous Vehicle Based on MPC with Variable Predictive Horizon[J]. Journal of Mechanical Engineering, 2022, 58(24): 275-288.

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基于变预测时域MPC的自动驾驶汽车轨迹跟踪控制研究

Research on Trajectory Tracking Control of Autonomous Vehicle Based on MPC with Variable Predictive Horizon

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