Event-triggered output feedback containment control for a class of stochastic nonlinear multi-agent systems

Journal article

Yang, Yang, Xi, Xiaorui, Miao, Songtao and Wu, Jinran. (2022). Event-triggered output feedback containment control for a class of stochastic nonlinear multi-agent systems. Applied Mathematics and Computation. 418, p. Article 126817. https://doi.org/10.1016/j.amc.2021.126817
AuthorsYang, Yang, Xi, Xiaorui, Miao, Songtao and Wu, Jinran
Abstract

An output feedback-based containment control strategy is proposed with an event-triggered mechanism for a class of stochastic nonlinear multi-agent systems (MASs). For unavailable internal states of individual agent, a state observer is constructed for their estimations. The dynamic surface control (DSC) technology is employed to improve traditional backstepping, where a first-order filter is introduced for derivation calculation of virtual control laws. For the purpose of reducing communication resources, a fixed threshold-based triggered strategy is presented to reduce data transmission bits over communication channels. It is proven that all signals in the closed-loop system are bounded in probability and the containment control is achieved. Finally, the effectiveness of the proposed control strategy is verified via two examples.

Keywordscontainment; dynamic surface; event-triggered; neural networks; stochastic multi-agent systems
Year2022
JournalApplied Mathematics and Computation
Journal citation418, p. Article 126817
PublisherElsevier Inc.
ISSN0096-3003
Digital Object Identifier (DOI)https://doi.org/10.1016/j.amc.2021.126817
Scopus EID2-s2.0-85120951173
Open accessPublished as green open access
Page range1-19
FunderNational Natural Science Foundation of China (NSFC)
Natural Science Foundation of Jiangsu Province
Nanjing University of Posts and Telecommunications (NUPTSF), China
Australian Research Council (ARC)
Author's accepted manuscript
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Open
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All rights reserved
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Controlled
Output statusPublished
Publication dates
Online11 Dec 2021
Publication process dates
Accepted17 Nov 2021
Deposited07 Jul 2023
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant ID61873130
BK20191377
NY220102
NY220194
2020XZZ11
CE140100049
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