English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Event-Triggered Adaptive Containment Control for Heterogeneous Stochastic Nonlinear Multiagent Systems

Wang, X., Xu, R., Huang, T., Kurths, J. (2024): Event-Triggered Adaptive Containment Control for Heterogeneous Stochastic Nonlinear Multiagent Systems. - IEEE Transactions on Neural Networks and Learning Systems, 35, 6, 8524-8534.
https://doi.org/10.1109/TNNLS.2022.3230508

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Wang, Xin 1, Author
Xu, Rui 1, Author
Huang, Tingwen 1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: This article investigates the event-triggered adaptive containment control problem for a class of stochastic nonlinear multiagent systems with unmeasurable states. A stochastic system with unknown heterogeneous dynamics is established to describe the agents in a random vibration environment. Besides, the uncertain nonlinear dynamics are approximated by radial basis function neural networks (NNs), and the unmeasured states are estimated by constructing the NN-based observer. In addition, the switching-threshold-based event-triggered control method is adopted with the hope of reducing communication consumption and balancing system performance and network constraints. Moreover, we develop the novel distributed containment controller by utilizing the adaptive backstepping control strategy and the dynamic surface control (DSC) approach such that the output of each follower converges to the convex hull spanned by multiple leaders, and all signals of the closed-loop system are cooperatively semi-globally uniformly ultimately bounded in mean square. Finally, we verify the efficiency of the proposed controller by the simulation examples.

Details

show
hide
Language(s): eng - English
 Dates: 2023-01-032024-06-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TNNLS.2022.3230508
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: IEEE Transactions on Neural Networks and Learning Systems
Source Genre: Journal, SCI, Scopus
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 35 (6) Sequence Number: - Start / End Page: 8524 - 8534 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-on-neural-networks-and-learning-systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)