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

  • Type:


  • Date:

    17 May 2022

  • Publication Status:

    In Press

  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:


  • Cross Ref:


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  • Funders:

    National Natural Science Foundation of China; Natural Sciences and Engineering Research Council of Canada; Natural Science Research Project of Higher Education in Jiangsu Province


Zhao, H., Shan, J., Peng, L., & Yu, H. (in press). Learning-based Robust Bipartite Consensus Control for a Class of Multiagent Systems. IEEE Transactions on Industrial Electronics,



Multiagent systems, bipartite consensus, data-driven control, data dropout, neural networks

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