Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method

Qing WANG; Lei SHI; Guibin SUN; Zhaoxin FAN; Shimin WANG; Fanglong YAO

doi:10.1109/ICUS66297.2025.11295815

Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method

Qing WANG^*
, Lei SHI
, Guibin SUN
, Zhaoxin FAN
, Shimin WANG
, Fanglong YAO

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

Abstract

This letter investigates the flocking behavior of multi-agent systems (MASs) on cooperation-competition networks by utilizing a data-driven iterative learning approach. A weighted signum-function is applied to elucidate the cooperation-competition relationships between agents, and a leader-follower based error model is designed. A cluster behavior distributed control protocol is constructed using only the input and output data of agents. With the help of model-free adaptive iterative learning control (MFAILC) theory, the error system is comprehensively analyzed and algebraic conditions for achieving the flocking behavior are established. Finally, the performance of the proposed control protocol in terms of the flocking behavior is verified by numerical simulation analysis.

Original language	English
Title of host publication	Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025
Editors	Rong SONG
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2032-2037
Number of pages	6
ISBN (Electronic)	9798331526726
DOIs	https://doi.org/10.1109/ICUS66297.2025.11295815
Publication status	Published - 2025
Externally published	Yes
Event	2025 IEEE International Conference on Unmanned Systems, ICUS 2025 - Changzhou, China Duration: 18 Sept 2025 → 19 Sept 2025

Publication series

Name	Proceedings of IEEE International Conference on Unmanned Systems, ICUS 2025
Publisher	IEEE
ISSN (Print)	2771-7364
ISSN (Electronic)	2771-7372

Conference

Conference	2025 IEEE International Conference on Unmanned Systems, ICUS 2025
Country/Territory	China
City	Changzhou
Period	18/09/25 → 19/09/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Funding

This work was supported by the Postdoctoral Fellowship Program of CPSF under Grant Number BX20240462 .

Keywords

data-driven iterative learning
distributed control
flocking
model-free adaptive

Access to Document

10.1109/ICUS66297.2025.11295815

Cite this

WANG, Q., SHI, L., SUN, G., FAN, Z., WANG, S., & YAO, F. (2025). Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method. In R. SONG (Ed.), Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025 (pp. 2032-2037). (Proceedings of IEEE International Conference on Unmanned Systems, ICUS 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS66297.2025.11295815

WANG, Qing ; SHI, Lei ; SUN, Guibin et al. / Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method. Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025. editor / Rong SONG. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 2032-2037 (Proceedings of IEEE International Conference on Unmanned Systems, ICUS 2025).

@inproceedings{c0a63aa4f91a499e8472e3282783eaf1,

title = "Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method",

abstract = "This letter investigates the flocking behavior of multi-agent systems (MASs) on cooperation-competition networks by utilizing a data-driven iterative learning approach. A weighted signum-function is applied to elucidate the cooperation-competition relationships between agents, and a leader-follower based error model is designed. A cluster behavior distributed control protocol is constructed using only the input and output data of agents. With the help of model-free adaptive iterative learning control (MFAILC) theory, the error system is comprehensively analyzed and algebraic conditions for achieving the flocking behavior are established. Finally, the performance of the proposed control protocol in terms of the flocking behavior is verified by numerical simulation analysis.",

keywords = "data-driven iterative learning, distributed control, flocking, model-free adaptive",

author = "Qing WANG and Lei SHI and Guibin SUN and Zhaoxin FAN and Shimin WANG and Fanglong YAO",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Unmanned Systems, ICUS 2025 ; Conference date: 18-09-2025 Through 19-09-2025",

year = "2025",

doi = "10.1109/ICUS66297.2025.11295815",

language = "English",

series = "Proceedings of IEEE International Conference on Unmanned Systems, ICUS 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2032--2037",

editor = "Rong SONG",

booktitle = "Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025",

address = "United States",

}

WANG, Q, SHI, L, SUN, G, FAN, Z, WANG, S & YAO, F 2025, Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method. in R SONG (ed.), Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025. Proceedings of IEEE International Conference on Unmanned Systems, ICUS 2025, Institute of Electrical and Electronics Engineers Inc., pp. 2032-2037, 2025 IEEE International Conference on Unmanned Systems, ICUS 2025, Changzhou, China, 18/09/25. https://doi.org/10.1109/ICUS66297.2025.11295815

Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method. / WANG, Qing; SHI, Lei; SUN, Guibin et al.
Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025. ed. / Rong SONG. Institute of Electrical and Electronics Engineers Inc., 2025. p. 2032-2037 (Proceedings of IEEE International Conference on Unmanned Systems, ICUS 2025).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method

AU - WANG, Qing

AU - SHI, Lei

AU - SUN, Guibin

AU - FAN, Zhaoxin

AU - WANG, Shimin

AU - YAO, Fanglong

PY - 2025

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N2 - This letter investigates the flocking behavior of multi-agent systems (MASs) on cooperation-competition networks by utilizing a data-driven iterative learning approach. A weighted signum-function is applied to elucidate the cooperation-competition relationships between agents, and a leader-follower based error model is designed. A cluster behavior distributed control protocol is constructed using only the input and output data of agents. With the help of model-free adaptive iterative learning control (MFAILC) theory, the error system is comprehensively analyzed and algebraic conditions for achieving the flocking behavior are established. Finally, the performance of the proposed control protocol in terms of the flocking behavior is verified by numerical simulation analysis.

AB - This letter investigates the flocking behavior of multi-agent systems (MASs) on cooperation-competition networks by utilizing a data-driven iterative learning approach. A weighted signum-function is applied to elucidate the cooperation-competition relationships between agents, and a leader-follower based error model is designed. A cluster behavior distributed control protocol is constructed using only the input and output data of agents. With the help of model-free adaptive iterative learning control (MFAILC) theory, the error system is comprehensively analyzed and algebraic conditions for achieving the flocking behavior are established. Finally, the performance of the proposed control protocol in terms of the flocking behavior is verified by numerical simulation analysis.

KW - data-driven iterative learning

KW - distributed control

KW - flocking

KW - model-free adaptive

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DO - 10.1109/ICUS66297.2025.11295815

M3 - Conference paper (refereed)

AN - SCOPUS:105031915051

T3 - Proceedings of IEEE International Conference on Unmanned Systems, ICUS 2025

SP - 2032

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BT - Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2025 IEEE International Conference on Unmanned Systems, ICUS 2025

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ER -

WANG Q, SHI L, SUN G, FAN Z, WANG S, YAO F. Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method. In SONG R, editor, Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 2032-2037. (Proceedings of IEEE International Conference on Unmanned Systems, ICUS 2025). doi: 10.1109/ICUS66297.2025.11295815

Multi-Agent Flocking Formation over Cooperation-Competition Networks: A Data-Driven Iterative Learning Method

Abstract

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Keywords

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