Antagonistic Interaction-Based Bipartite Consensus Control for Heterogeneous Networked Systems

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

doi:10.1109/TSMC.2022.3167120

Antagonistic Interaction-Based Bipartite Consensus Control for Heterogeneous Networked Systems

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

Research output: Contribution to journal › Article › peer-review

45 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 language	English
Pages (from-to)	71-81
Number of pages	11
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	53
Issue number	1
DOIs	https://doi.org/10.1109/TSMC.2022.3167120
Publication status	Published - 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.

Keywords

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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10.1109/TSMC.2022.3167120

Cite this

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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.",

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author = "Guangliang Liu and Hongjing Liang and Yingnan Pan and Ahn, {Choon Ki}",

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N1 - 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.

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N2 - 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.

AB - 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.

KW - Antagonistic interactions

KW - bipartite consensus control

KW - heterogeneous networked systems

KW - neural networks (NNs)

KW - unknown backlash-like hysteresis

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Antagonistic Interaction-Based Bipartite Consensus Control for Heterogeneous Networked Systems

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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