Distributed fixed-time Nash equilibrium seeking algorithm for multiple ASVs: A hybrid event-triggered approach

被引:3
|
作者
Hua, Menghu [1 ]
Ding, Hua-Feng [1 ]
Yao, Xiang-Yu [1 ]
Liu, Wen-Jin [1 ]
机构
[1] China Univ Geosci, Sch Mech Engn & Elect Informat, Wuhan 430074, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Fixed-time control; Hybrid event-triggered control; Multiple autonomous surface vehicles; Noncooperative game; Nash equilibrium seeking; TRAJECTORY TRACKING; VEHICLES;
D O I
10.1016/j.oceaneng.2023.116410
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
In this article, the fixed-time Nash equilibrium (FTNE) seeking problem of multiple autonomous surface vehicles (ASVs) is considered. As individuals fight for an allocation of specific shared but restricted resources, two novel FTNE seeking algorithms are designed for this topic through an error decomposition method. First, a model-based control algorithm with only one convergence time function in a single-layer structure is designed to achieve Nash equilibrium within a fixed time. Then, a FTNE algorithm with a three-layer structure is presented for multiple ASVs subject to system uncertainties. In particular, this paper proposes a hybrid fixed -time event-triggered approach for reducing controller update frequencies and communication frequencies of two algorithms simultaneously, which is capable of decreasing resource consumption in a constrained resource environment. Furthermore, rigorous sufficient criteria are established for fixed-time convergence through Lyapunov stability analysis, and the upper bound on settling time without the requirement of initial conditions is derived. Finally, simulation examples are given to demonstrate the viability of two algorithms.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Distributed event-triggered fixed-time formation and trajectory tracking control for multiple stratospheric airships
    Zhang, Yifei
    Zhu, Ming
    Chen, Tian
    Zheng, Zewei
    ISA TRANSACTIONS, 2022, 130 : 63 - 78
  • [22] Finite/fixed-time practical sliding mode: An event-triggered approach
    Song, Feida
    Wang, Leimin
    Wang, Qingyi
    Wen, Shiping
    INFORMATION SCIENCES, 2023, 631 : 241 - 255
  • [23] Hybrid Nash Equilibrium Seeking Under Partial-Decision Information: An Adaptive Dynamic Event-Triggered Approach
    Xu, Wenying
    Wang, Zidong
    Hu, Guoqiang
    Kurths, Jurgen
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2023, 68 (10) : 5862 - 5876
  • [24] Distributed fixed-time nonlinear control of microgrids based on event-triggered strategy
    Chen, Gang
    Xiang, Hongbing
    NONLINEAR DYNAMICS, 2023, 111 (21) : 19931 - 19946
  • [25] Distributed economic dispatch control in smart grid based on fixed-time dynamic event-triggered algorithm
    Ji, Lianghao
    Xu, Zhenxiang
    Yang, Shasha
    Guo, Xing
    Li, Huaqing
    ELECTRIC POWER SYSTEMS RESEARCH, 2024, 236
  • [26] Distributed fixed-time nonlinear control of microgrids based on event-triggered strategy
    Gang Chen
    Hongbing Xiang
    Nonlinear Dynamics, 2023, 111 : 19931 - 19946
  • [27] Distributed Event-triggered Strategy for Fixed-time Economic Dispatch in Islanded Microgrids
    Liu, Haoran
    Fan, Huijin
    Wang, Bo
    PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 1401 - 1406
  • [28] Distributed economic dispatch algorithm in smart grid based on event-triggered and fixed-time consensus methods
    Ji, Lianghao
    Zhang, Linlong
    Zhang, Cuijuan
    Yang, Shasha
    Guo, Xing
    Li, Huaqing
    NEUROCOMPUTING, 2024, 572
  • [29] Event-triggered fixed-time adaptive neural formation control for underactuated ASVs with connectivity constraints and prescribed performance
    Liu, Haitao
    Lin, Jianfei
    Li, Ronghui
    Tian, Xuehong
    Mai, Qingqun
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (18): : 13485 - 13501
  • [30] Distributed fixed-time dynamic event-triggered leaderless formation control for multiple AUVs based on FRBFDO
    Meng, Chuncheng
    Mo, Taiping
    Zhang, Xiangwen
    OCEAN ENGINEERING, 2024, 307