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Fully distributed dynamic event-triggered output regulation for heterogeneous linear multiagent systems under fixed and switching topologies

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Abstract

This paper focuses on the cooperative output regulation issues of heterogeneous linear multiagent systems under fixed and arbitrary connected switching topologies, respectively. Firstly, the fully distributed node-based event-triggered compensator under fixed topology and edge-based event-triggered compensator under arbitrary connected switching topologies are proposed, where two novel dynamic event-triggered mechanisms to ensure discrete communication between agents, respectively. Note that the internal dynamic variables included in the triggering mechanisms play an important role in reducing the communication frequency among neighbors. In addition, under these triggering mechanisms, the interval of adjacent interaction events of each agent is strictly greater than zero. Then, based on the proposed compensators, the fully distributed state and measured output feedback control protocols are designed to solve the cooperative output regulation problem. Finally, several examples are performed to validate the main results of this paper.

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Acknowledgements

This work was supported by National Key R&D Program of China under grant 2018YFA0702200, and National Natural Science Foundation of China (61627809), and Liaoning Revitalization Talents Program (XLYC1801005). The Nature Science Foundation of Liaoning Province of China (2022JH25/10100008).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110004, Liaoning, China

    Zilong Tan, Juan Zhang, Yuqing Yan, Jiayue Sun & Huaguang Zhang

  2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110004, Liaoning, China

    Jiayue Sun & Huaguang Zhang

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  1. Zilong Tan
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  2. Juan Zhang
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  3. Yuqing Yan
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Correspondence to Jiayue Sun.

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Tan, Z., Zhang, J., Yan, Y. et al. Fully distributed dynamic event-triggered output regulation for heterogeneous linear multiagent systems under fixed and switching topologies. Neural Comput & Applic 35, 11753–11771 (2023). https://doi.org/10.1007/s00521-023-08318-1

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