可变构型无人地面车辆的无模型预测路径跟踪控制

Model free predictive path tracking control of variable-configuration unmanned ground vehicle.

作者信息

Jiang Yue, Xu Xiaojun, Zhang Lei, Zou Tengan

机构信息

College of Intelligent Science, National University of Defense Technology, Changsha 410073, China.

出版信息

ISA Trans. 2022 Oct;129(Pt A):485-494. doi: 10.1016/j.isatra.2022.01.026. Epub 2022 Feb 1.

DOI:10.1016/j.isatra.2022.01.026

PMID:35148886

Abstract

Unmanned ground vehicle (UGV) is developing towards high mobility and intelligence, where path tracking plays a particularly important role. This paper investigated the path tracking control strategy of variable-configuration unmanned ground vehicle. In order to overcome the structural and unstructured uncertainties, a model free predictive control (MFAPC) strategy using particle swarm optimization (PSO) is presented. The control scheme of MFAPC is improved by integrating vehicle state parameters. Then, the main parameters of the improved control scheme are optimized by PSO algorithm. The effectiveness of the proposed method under different operation conditions is verified by simulation. The experimental results show that the proposed scheme does not require the accurate mathematical model and can quickly track the reference path.

摘要

无人地面车辆（UGV）正朝着高机动性和智能化方向发展，其中路径跟踪起着尤为重要的作用。本文研究了可变构型无人地面车辆的路径跟踪控制策略。为了克服结构和非结构不确定性，提出了一种基于粒子群优化（PSO）的无模型预测控制（MFAPC）策略。通过整合车辆状态参数对MFAPC的控制方案进行了改进。然后，利用PSO算法对改进后的控制方案的主要参数进行了优化。通过仿真验证了所提方法在不同运行条件下的有效性。实验结果表明，所提方案不需要精确的数学模型，能够快速跟踪参考路径。