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Data-driven cooperative output regulation of multi-agent systems under distributed denial of service attacks

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Abstract

This paper addresses an optimal, cooperative output regulation problem for multi-agent systems with distributed denial of service attacks and unknown system dynamics. Unlike existing studies, the proposed solution is essentially a learning-based control strategy such that one can obtain a distributed control policy with internal models through online data and analyze the resilience of closed-loop systems, both without the precise knowledge of system dynamics in the state-space model. The efficiency of the proposed methodology is validated using computer simulations.

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Acknowledgements

The work was supported in part by Science and Technology Major Project 2020 of Liaoning Province (Grant No. 2020JH1/10100008), National Natural Science Foundation of China (Grant Nos. 61991404, 61991400), 111 Project 2.0 (Grant No. B08015), and U.S. National Science Foundation (Grant No. CNS-2227153).

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Authors and Affiliations

  1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China

    Weinan Gao

  2. Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA

    Zhong-Ping Jiang

Authors
  1. Weinan Gao
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  2. Zhong-Ping Jiang
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Correspondence to Weinan Gao or Zhong-Ping Jiang.

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Gao, W., Jiang, ZP. Data-driven cooperative output regulation of multi-agent systems under distributed denial of service attacks. Sci. China Inf. Sci. 66, 190201 (2023). https://doi.org/10.1007/s11432-022-3702-4

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  • DOI: https://doi.org/10.1007/s11432-022-3702-4

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