Distributed adaptive optimization-based formation tracking with double parameter projections for multi-agent systems

被引:7
|
作者
Peng, Zhaoxia [1 ]
Wu, Bofan [1 ]
Wen, Guoguang [2 ]
Yang, Shichun [1 ]
Huang, Tingwen [3 ]
机构
[1] Beihang Univ, Sch Transportat Sci & Engn, Beijing 100083, Peoples R China
[2] Beijing Jiaotong Univ, Dept Math, Beijing 100044, Peoples R China
[3] Texas A&M Univ Qatar, Doha 23874, Qatar
来源
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2022年 / 359卷 / 11期
基金
中国国家自然科学基金;
关键词
VARYING FORMATION TRACKING; PREDICTIVE CONTROL; AVOIDANCE;
D O I
10.1016/j.jfranklin.2022.05.041
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a distributed adaptive optimization-based formation tracking strategy with double parameter projections for multi-agent systems is addressed under a centralized task allocation and distributed task execution (CTA-DTE) framework. Since a pre-described formation strategy is unable to adapt to a complex and dynamic environment, an optimization-based approach is proposed to transfer the formation tracking problem into a time-varying optimization one, subject to some constraints with several time-varying parameters which determine the rule of formation configuration change adaptively. These parameters are computed by a centralized unit and allocated to each agent as a global mission. Furthermore, each agent cooperates with others to execute this mission under a distributed optimization-based strategy, which combines a geometric center observer technology and a novel double parameter projections technology. The former ensures accurate tracking of a continuous reference trajectory. The latter guarantees that all agents enter into a time-varying security region and never escape from it, and meanwhile, all agents are attracted towards the best time-varying formation configuration via a gradient descent with a compensation. Finally, some simulation results are illustrated to verify the effectiveness of the strategy. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:5251 / 5271
页数:21
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