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Observer-based memory consensus for nonlinear multi-agent systems with output quantization and Markov switching topologies

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  • Parivallal, A.
  • Sakthivel, R.
  • Amsaveni, R.
  • Alzahrani, Faris
  • Alshomrani, Ali Saleh

Abstract

This paper focuses on the issue of observer based memory consensus design for nonlinear multi-agent systems (MASs) with quantization effects and Markov switching topologies. Notably, an observer based memory consensus protocol with quantization in measurement output is proposed to achieve the consensus of considered nonlinear MASs. Throughout this paper, undirected graph is used to describe the interaction between the neighboring agents. By using Lyapunov technique together with the algebraic graph theory properties, a group of new sufficient conditions is derived in the form of linear matrix inequalities (LMIs) to obtain the consensus of considered nonlinear MASs. Especially, the controller and observer gain matrices are obtained by solving the derived LMIs. Finally, two numerical examples are provided to establish the capability of proposed observer-based consensus protocol.

Suggested Citation

  • Parivallal, A. & Sakthivel, R. & Amsaveni, R. & Alzahrani, Faris & Alshomrani, Ali Saleh, 2020. "Observer-based memory consensus for nonlinear multi-agent systems with output quantization and Markov switching topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    • Handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437119321879
    DOI: 10.1016/j.physa.2019.123949
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    References listed on IDEAS

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    1. Sun, Yongzheng & Zhao, Donghua & Ruan, Jiong, 2010. "Consensus in noisy environments with switching topology and time-varying delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(19), pages 4149-4161.
    2. Hu, Xiaohui & Xia, Jianwei & Wei, Yunliang & Meng, Bo & Shen, Hao, 2019. "Passivity-based state synchronization for semi-Markov jump coupled chaotic neural networks with randomly occurring time delays," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 32-41.
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