基于分布式事件触发策略的异构非线性多智能体系统包含控制

孙雅妮; 邹文成; 郭健; 向峥嵘

doi:10.1631/FITEE.2000034

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基于分布式事件触发策略的异构非线性多智能体系统包含控制

复杂网络和系统的分布式滤波与控制专辑 | Updated：2022-05-19

- 基于分布式事件触发策略的异构非线性多智能体系统包含控制
- Containment control for heterogeneous nonlinear multi-agent systems under distributed event-triggered schemes
- 信息与电子工程前沿（英文） 2021年22卷第1期页码：107-119
- Affiliations：
  
  School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
- Author bio：
  
  XIANG Zheng-rong, E-mail: xiangzr@njust.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61873128 and 61673219) and the Jiangsu Key Research and Development Plan, China (No. BE2018004-3)
- DOI：10.1631/FITEE.2000034
  中图分类号： TP13
- 收稿：2020-01-18，
  
  修回：2020-;8-18，
  
  纸质出版：2021-01
- Accepted：
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孙雅妮, 邹文成, 郭健, 等. 基于分布式事件触发策略的异构非线性多智能体系统包含控制[J]. 信息与电子工程前沿（英文）, 2021,22(1):107-119.

SUN Ya-ni, ZOU Wen-cheng, GUO Jian, et al. Containment control for heterogeneous nonlinear multi-agent systems under distributed event-triggered schemes[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 107-119.
孙雅妮, 邹文成, 郭健, 等. 基于分布式事件触发策略的异构非线性多智能体系统包含控制[J]. 信息与电子工程前沿（英文）, 2021,22(1):107-119. DOI： 10.1631/FITEE.2000034.

SUN Ya-ni, ZOU Wen-cheng, GUO Jian, et al. Containment control for heterogeneous nonlinear multi-agent systems under distributed event-triggered schemes[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 107-119. DOI： 10.1631/FITEE.2000034.

摘要

研究分布式事件触发策略下高阶异构非线性多智能体系统包含控制问题。为实现包含控制目标并减少智能体间的通讯损耗，采用反推控制法、李雅普诺夫函数分析法和神经网络，提出一种分布式事件触发控制策略。为避免对状态误差的连续监测，将事件触发情形的结论推广到自触发控制情形。所提控制协议和触发规则确保每个智能体的输出能在一定误差范围内收敛到有多个领导者信号组成的凸包中。此外，所有智能体都不会发生Zeno现象。最后，通过两个数值仿真案例证明所得结论的正确性。

Abstract

We study the containment control problem for high-order heterogeneous nonlinear multi-agent systems under distributed event-triggered schemes. To achieve the containment control objective and reduce communication consumption among agents

a distributed event-triggered control scheme is proposed by applying the backstepping method

Lyapunov functional approach

and neural networks. Then

the results are extended to the self-triggered control case to avoid continuous monitoring of state errors. The developed protocols and triggered rules ensure that the output for each follower converges to the convex hull spanned by multi-leader signals within a bounded error. In addition

no agent exhibits Zeno behavior. Two numerical simulations are finally presented to verify the correctness of the obtained results.

关键词

Keywords

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