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

被引:6
|
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
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
下载
收藏
页码:5251 / 5271
页数:21
相关论文
共 50 条
  • [31] Distributed adaptive finite-time tracking for multi-agent systems and its application
    Li, Peiming
    Wu, Xiang
    Chen, Xiangyong
    Qiu, Jianlong
    NEUROCOMPUTING, 2022, 481 : 46 - 54
  • [32] Consensus tracking for multi-agent systems with directed graph via distributed adaptive protocol
    Chu, Hongjun
    Cai, Yunze
    Zhang, Weidong
    NEUROCOMPUTING, 2015, 166 : 8 - 13
  • [33] Distributed Adaptive Tracking Control of Non-affine Nonlinear Multi-agent Systems
    Zhao, Ya
    Chen, Gang
    PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, : 1518 - 1523
  • [34] Distributed adaptive tracking consensus control for a class of heterogeneous nonlinear multi-agent systems
    Fan, Yongqing
    Zhang, Yu
    Li, Zhen
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2025, 227 : 420 - 441
  • [35] Distributed adaptive consensus tracking control for nonlinear multi-agent systems with state constraints
    Zhang, Yanhui
    Liang, Hongjing
    Ma, Hui
    Zhou, Qi
    Yu, Zhandong
    APPLIED MATHEMATICS AND COMPUTATION, 2018, 326 : 16 - 32
  • [36] Distributed Adaptive Output Feedback Consensus Tracking for Multi-Agent Systems with Unknown Nonlinearities
    Sun Junyong
    Geng Zhiyong
    2015 34TH CHINESE CONTROL CONFERENCE (CCC), 2015, : 6866 - 6871
  • [37] Distributed Adaptive Control for Nonlinear Multi-agent Systems
    Li Wuquan
    Gu Jianzhong
    PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 7862 - 7866
  • [38] Observer-based Distributed Optimization for Linear Multi-agent Systems
    Zhang, Yu
    Chen, Wenhai
    Gao, Lixin
    PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 3430 - 3435
  • [39] Consensus-based formation tracking of fuzzy multi-agent systems
    Lee, H.J. (mylchi@inha.ac.kr), 1600, Korean Institute of Electrical Engineers (63):
  • [40] Distributed Velocity Observer based Formation Control for Multi-agent Systems
    Luo Xiaoyuan
    Li Xu
    Li Xiaolei
    Guan Xinping
    2014 33RD CHINESE CONTROL CONFERENCE (CCC), 2014, : 1746 - 1750