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Learning-based Robust Bipartite Consensus Control for a Class of Multiagent Systems

Zhao, Huarong; Shan, Jinjun; Peng, Li; Yu, Hongnian

Authors

Huarong Zhao

Jinjun Shan

Li Peng



Abstract

This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multi-agent systems (MASs) with fixed and switching topologies against data dropout and unknown disturbances. At first, the controlled system's virtual linear data model is developed by employing the pseudo partial derivative technique, and a distributed combined measurement error function is established utilizing a signed graph theory. Then, an input gain compensation scheme is formulated to mitigate the effects of data dropout in both feedback and forward channels. Moreover, a data-driven learning-based robust bipartite consensus control (LRBCC) scheme based on a radial basis function neural network observer is proposed to estimate the unknown disturbance, using the online input/output data without requiring any information on the mathematical dynamics. The stability analysis of the proposed LRBCC approach is given. Simulation and hardware testing also illustrate the correctness and effectiveness of the designed method.

Journal Article Type Article
Acceptance Date Mar 1, 2022
Online Publication Date May 17, 2022
Publication Date 2023-04
Deposit Date Jun 15, 2022
Publicly Available Date Jun 16, 2022
Journal IEEE Transactions on Industrial Electronics
Print ISSN 0278-0046
Electronic ISSN 1557-9948
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 70
Issue 4
Pages 4068-4076
DOI https://doi.org/10.1109/tie.2022.3174275
Keywords Multiagent systems, bipartite consensus, data-driven control, data dropout, neural networks
Public URL http://researchrepository.napier.ac.uk/Output/2879013

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