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

Di Cui; Huiping Li; Panfeng Huang

doi:10.1007/978-981-99-6498-7_43

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

Di Cui, Huiping Li, Panfeng Huang

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings
Editors	Huayong Yang, Jun Zou, Geng Yang, Xiaoping Ouyang, Honghai Liu, Zhouping Yin, Lianqing Liu, Zhiyong Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	501-512
Number of pages	12
ISBN (Print)	9789819964970
DOIs	https://doi.org/10.1007/978-981-99-6498-7_43
State	Published - 2023
Event	16th International Conference on Intelligent Robotics and Applications, ICIRA 2023 - Hangzhou, China Duration: 5 Jul 2023 → 7 Jul 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14273 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Intelligent Robotics and Applications, ICIRA 2023
Country/Territory	China
City	Hangzhou
Period	5/07/23 → 7/07/23

Keywords

Event-triggered
Large-scale swarm
Mean-filed model
Model predictive control

Access to Document

10.1007/978-981-99-6498-7_43

Cite this

Cui, D., Li, H., & Huang, P. (2023). Event-Triggered Model Predictive Mean-Field Control for Stabilizing Robotic Swarm. In H. Yang, J. Zou, G. Yang, X. Ouyang, H. Liu, Z. Yin, L. Liu, & Z. Wang (Eds.), Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings (pp. 501-512). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14273 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-6498-7_43

Cui, Di ; Li, Huiping ; Huang, Panfeng. / Event-Triggered Model Predictive Mean-Field Control for Stabilizing Robotic Swarm. Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings. editor / Huayong Yang ; Jun Zou ; Geng Yang ; Xiaoping Ouyang ; Honghai Liu ; Zhouping Yin ; Lianqing Liu ; Zhiyong Wang. Springer Science and Business Media Deutschland GmbH, 2023. pp. 501-512 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Event-Triggered Model Predictive Mean-Field Control for Stabilizing Robotic Swarm",

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{\textquoteright}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.",

keywords = "Event-triggered, Large-scale swarm, Mean-filed model, Model predictive control",

author = "Di Cui and Huiping Li and Panfeng Huang",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.; 16th International Conference on Intelligent Robotics and Applications, ICIRA 2023 ; Conference date: 05-07-2023 Through 07-07-2023",

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Cui, D, Li, H & Huang, P 2023, Event-Triggered Model Predictive Mean-Field Control for Stabilizing Robotic Swarm. in H Yang, J Zou, G Yang, X Ouyang, H Liu, Z Yin, L Liu & Z Wang (eds), Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14273 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 501-512, 16th International Conference on Intelligent Robotics and Applications, ICIRA 2023, Hangzhou, China, 5/07/23. https://doi.org/10.1007/978-981-99-6498-7_43

Event-Triggered Model Predictive Mean-Field Control for Stabilizing Robotic Swarm. / Cui, Di; Li, Huiping ; Huang, Panfeng.
Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings. ed. / Huayong Yang; Jun Zou; Geng Yang; Xiaoping Ouyang; Honghai Liu; Zhouping Yin; Lianqing Liu; Zhiyong Wang. Springer Science and Business Media Deutschland GmbH, 2023. p. 501-512 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14273 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Cui D, Li H , Huang P. Event-Triggered Model Predictive Mean-Field Control for Stabilizing Robotic Swarm. In Yang H, Zou J, Yang G, Ouyang X, Liu H, Yin Z, Liu L, Wang Z, editors, Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 501-512. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-6498-7_43