时变网络的收敛性及其应用

王庆领

doi:10.1631/FITEE.2000160

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时变网络的收敛性及其应用

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

- 时变网络的收敛性及其应用
- Convergence of time-varying networks and its applications
- 信息与电子工程前沿（英文） 2021年22卷第1期页码：88-96
- Affiliations：
  
  School of Automation, Southeast University, Nanjing 210096, China
- Author bio：
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61973074, U1713209, 61520106009, and 61533008), the National Key R & D Program of China (No. 2018AAA0101400), the Science and Technology on Information System Engineering Laboratory, China (No. 05201902), and the Fundamental Research Funds for the Central Universities, China
- DOI：10.1631/FITEE.2000160
  中图分类号： TP13
- 收稿：2020-04-08，
  
  修回：2020-;8-;6，
  
  纸质出版：2021-01
- Accepted：
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王庆领. 时变网络的收敛性及其应用[J]. 信息与电子工程前沿（英文）, 2021,22(1):88-96.

WANG Qingling. Convergence of time-varying networks and its applications[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 88-96.
王庆领. 时变网络的收敛性及其应用[J]. 信息与电子工程前沿（英文）, 2021,22(1):88-96. DOI： 10.1631/FITEE.2000160.

WANG Qingling. Convergence of time-varying networks and its applications[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 88-96. DOI： 10.1631/FITEE.2000160.

摘要

本文分析一类时变网络的收敛性。将收敛性质应用于具有未知控制方向的非线性多智能体系统协同控制问题，并提出一类新的基于Nussbaum型函数的控制策略。若时变网络是准平衡的，则所提控制策略可实现具有未知控制方向的非线性多智能体系统的收敛。所提算法的一个重要特点是，该算法可采用经典的Nussbaum型函数解决控制方向未知且不等同问题。最后，提出一个仿真案例验证所提算法的有效性。

Abstract

In this study

we present the convergence of time-varying networks. Then

we apply the convergence property to cooperative control of nonlinear multiagent systems (MASs) with unknown control directions (UCDs)

and illustrate a new kind of Nussbaum-type function based control algorithms. It is proven that if the time-varying networks are cut-balance

the convergence of nonlinear MASs with nonidentical UCDs is achieved using the presented algorithms. A critical feature of this application is that the designed algorithms can deal with nonidentical UCDs by employing conventional Nussbaum-type functions. Finally

one simulation example is given to illustrate the effectiveness of the presented algorithms.

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