Antagonistic Interaction-Based Bipartite Consensus Control for Heterogeneous Networked Systems

Guangliang Liu, Hongjing Liang, Yingnan Pan, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

This article investigates the bipartite consensus tracking control problem for nonlinear networked systems with antagonistic interactions and unknown backlash-like hysteresis. The generalized networked multiagent systems model is considered, in which every agent is an independent individual, and this model allows competitive and cooperative interactions to coexist. A Gaussian function is applied to simulate competition and cooperation among agents. Radial basis function (RBF) neural network (NN) is applied to estimate the unknown nonlinear function. By using backstepping technology, we propose an adaptive neural control protocol, which not only ensures that in the closed-loop system all the signals are bounded but also realizes bipartite consensus control. Finally, we present a simulation example to illustrate the effectiveness of the obtained result.

Original languageEnglish
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Adaptation models
  • Antagonistic interactions
  • Backstepping
  • bipartite consensus control
  • Consensus control
  • heterogeneous networked systems
  • Hysteresis
  • Multi-agent systems
  • neural networks (NNs)
  • Protocols
  • Topology
  • unknown backlash-like hysteresis

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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