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Event-Triggered Model Predictive Mean-Field Control for Stabilizing Robotic Swarm

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Intelligent Robotics and Applications (ICIRA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14273))

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Abstract

This paper investigates the resource-aware density regulation problem for a large-scale robotic swarm. A perturbed mean-field model(MFM) is first developed to describe the evolution process of the swarm’s actual density distribution (ADD) in a macroscopic manner, thus endowing the control algorithm with scalability property. A novel event-triggered (ET) model predictive mean-field control (MFC) algorithm is proposed to reduce the computation and communication burdens of agents while providing high control performance. Finally, by means of the numerical example, we verify the effectiveness of this algorithm.

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant U22B2039, 62273281; in part by Aoxiang Youth Scholar Program under Grant.

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Correspondence to Huiping Li .

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Cui, D., Li, H., Huang, P. (2023). Event-Triggered Model Predictive Mean-Field Control for Stabilizing Robotic Swarm. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14273. Springer, Singapore. https://doi.org/10.1007/978-981-99-6498-7_43

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  • DOI: https://doi.org/10.1007/978-981-99-6498-7_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6497-0

  • Online ISBN: 978-981-99-6498-7

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