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

27 Citations (Scopus)


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
Pages (from-to)71-81
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number1
Publication statusPublished - 2023 Jan 1

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62073046 and Grant 62003052, and in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (Ministry of Science and ICT) under Garnt NRF-2020R1A2C1005449.

Publisher Copyright:
© 2013 IEEE.


  • Antagonistic interactions
  • bipartite consensus control
  • heterogeneous networked systems
  • neural networks (NNs)
  • 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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