A novel adaptive trajectory tracking control for autonomous vehicles based on state expansion

被引：0

作者：

Wang, Jingye ^{[1
]}

Yu, Yuewei ^{[1
]}

Song, Yunpeng ^{[1
]}

Zhao, Leilei ^{[1
]}

机构：

[1] Shandong Univ Technol, Sch Transportat & Vehicle Engn, Zibo 255000, Peoples R China

来源：

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY | 2024年 / 38卷 / 06期

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Autonomous vehicle; Trajectory tracking; State extension; Model predictive control; Variable predictive horizon; MODEL-PREDICTIVE CONTROL;

D O I：

10.1007/s12206-024-0532-z

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

A tracking control algorithm for autonomous vehicles using state expansion is proposed. This algorithm, based on a three degrees of freedom (3-DOF) vehicle lateral dynamics model, extends the state variables of the traditional model predictive control (MPC) algorithm to an input-output dual feedback form based on the state expansion method. A dual feedback model predictive control (DF-MPC) algorithm was constructed. Based on this, taking the current speed and the curvature of the reference trajectory as the system input, and using the fuzzy control algorithm to dynamically adjust the prediction range of the DF-MPC trajectory tracking controller in real time, a variable predictive horizon double feedback model predictive control (VDF-MPC) trajectory tracking control method for autonomous vehicle was established. Through MATLAB/Simulink-CarSim joint simulation, the reliability of the established VDF-MPC trajectory tracking control method was verified.

引用

页码：3143 / 3154

页数：12

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