Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints

Junjie Fu; Guanghui Wen; Yuezu Lv; Tingwen Huang

doi:10.1007/978-3-030-36711-4_41

Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints

Junjie Fu^*, Guanghui Wen, Yuezu Lv, Tingwen Huang

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Consensus control of a class of high-order nonlinear multi-agent systems subject to multiple state constraints and input saturation is studied in this work. Barrier functions are employed to design a distributed controller which achieves consensus without violating the state constraints and input saturation provided that some feasibility conditions on the initial states and controller parameters are satisfied. The feasibility conditions can be checked off-line. Backstepping method and Lyapunov analysis are employed to study the convergence properties of the designed controller.

Original language	English
Title of host publication	Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
Editors	Tom Gedeon, Kok Wai Wong, Minho Lee
Publisher	Springer
Pages	492-503
Number of pages	12
ISBN (Print)	9783030367107
DOIs	https://doi.org/10.1007/978-3-030-36711-4_41
Publication status	Published - 2019
Externally published	Yes
Event	26th International Conference on Neural Information Processing, ICONIP 2019 - Sydney, Australia Duration: 12 Dec 2019 → 15 Dec 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11954 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Neural Information Processing, ICONIP 2019
Country/Territory	Australia
City	Sydney
Period	12/12/19 → 15/12/19

Keywords

Barrier function
Consensus
Input saturation
Multi-agent system
Nonlinear systems
State constraints

Access to Document

10.1007/978-3-030-36711-4_41

Cite this

Fu, J., Wen, G., Lv, Y., & Huang, T. (2019). Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings (pp. 492-503). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11954 LNCS). Springer. https://doi.org/10.1007/978-3-030-36711-4_41

Fu, Junjie ; Wen, Guanghui ; Lv, Yuezu et al. / Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints. Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. editor / Tom Gedeon ; Kok Wai Wong ; Minho Lee. Springer, 2019. pp. 492-503 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4d6aafc57cba47738112e26eb68279b0,

title = "Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints",

abstract = "Consensus control of a class of high-order nonlinear multi-agent systems subject to multiple state constraints and input saturation is studied in this work. Barrier functions are employed to design a distributed controller which achieves consensus without violating the state constraints and input saturation provided that some feasibility conditions on the initial states and controller parameters are satisfied. The feasibility conditions can be checked off-line. Backstepping method and Lyapunov analysis are employed to study the convergence properties of the designed controller.",

keywords = "Barrier function, Consensus, Input saturation, Multi-agent system, Nonlinear systems, State constraints",

author = "Junjie Fu and Guanghui Wen and Yuezu Lv and Tingwen Huang",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 26th International Conference on Neural Information Processing, ICONIP 2019 ; Conference date: 12-12-2019 Through 15-12-2019",

year = "2019",

doi = "10.1007/978-3-030-36711-4_41",

language = "English",

isbn = "9783030367107",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "492--503",

editor = "Tom Gedeon and Wong, {Kok Wai} and Minho Lee",

booktitle = "Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings",

address = "Germany",

}

Fu, J, Wen, G, Lv, Y & Huang, T 2019, Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints. in T Gedeon, KW Wong & M Lee (eds), Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11954 LNCS, Springer, pp. 492-503, 26th International Conference on Neural Information Processing, ICONIP 2019, Sydney, Australia, 12/12/19. https://doi.org/10.1007/978-3-030-36711-4_41

Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints. / Fu, Junjie; Wen, Guanghui; Lv, Yuezu et al.
Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. ed. / Tom Gedeon; Kok Wai Wong; Minho Lee. Springer, 2019. p. 492-503 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11954 LNCS).

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

TY - GEN

T1 - Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints

AU - Fu, Junjie

AU - Wen, Guanghui

AU - Lv, Yuezu

AU - Huang, Tingwen

PY - 2019

Y1 - 2019

N2 - Consensus control of a class of high-order nonlinear multi-agent systems subject to multiple state constraints and input saturation is studied in this work. Barrier functions are employed to design a distributed controller which achieves consensus without violating the state constraints and input saturation provided that some feasibility conditions on the initial states and controller parameters are satisfied. The feasibility conditions can be checked off-line. Backstepping method and Lyapunov analysis are employed to study the convergence properties of the designed controller.

AB - Consensus control of a class of high-order nonlinear multi-agent systems subject to multiple state constraints and input saturation is studied in this work. Barrier functions are employed to design a distributed controller which achieves consensus without violating the state constraints and input saturation provided that some feasibility conditions on the initial states and controller parameters are satisfied. The feasibility conditions can be checked off-line. Backstepping method and Lyapunov analysis are employed to study the convergence properties of the designed controller.

KW - Barrier function

KW - Consensus

KW - Input saturation

KW - Multi-agent system

KW - Nonlinear systems

KW - State constraints

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U2 - 10.1007/978-3-030-36711-4_41

DO - 10.1007/978-3-030-36711-4_41

M3 - Conference contribution

AN - SCOPUS:85076888371

SN - 9783030367107

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 492

EP - 503

BT - Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings

A2 - Gedeon, Tom

A2 - Wong, Kok Wai

A2 - Lee, Minho

PB - Springer

T2 - 26th International Conference on Neural Information Processing, ICONIP 2019

Y2 - 12 December 2019 through 15 December 2019

ER -

Fu J, Wen G, Lv Y, Huang T. Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints. In Gedeon T, Wong KW, Lee M, editors, Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. Springer. 2019. p. 492-503. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-36711-4_41

Barrier Function Based Consensus of High-Order Nonlinear Multi-agent Systems with State Constraints

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