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author:

Zhan, W. (Zhan, W..) [1] | Miao, Z. (Miao, Z..) [2] | Zhang, H. (Zhang, H..) [3] | Chen, Y. (Chen, Y..) [4] | Wu, Z.-G. (Wu, Z.-G..) [5] | He, W. (He, W..) [6] | Wang, Y. (Wang, Y..) [7]

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Scopus

Abstract:

This article presents a resilient formation control framework for networked nonholonomic mobile robots (NMRs) that enables long-time recovery abilities subject to denial-of-service (DoS) attacks by taking advantage of the Koopman operator. Due to the intermittent interruption of communication under DoS, the transmitted signals among the networked NMRs are incomplete. In the lifted space, the infinite-dimensional Koopman operator is employed to capture a linear characteristic of the missed signals from the available signals. Specifically, a data-driven cost function is developed to approximate the infinite-dimensional Koopman operator, allowing long-term recovery capabilities for the missed signals, where the useful historical data is identified by an event-triggered mechanism (ETM). Then, the least-squares method is implemented to calculate a finite-dimensional approximation of the Koopman operator. Once DoS attacks are active, the missed signals are recovered forward from the latest received signals through the approximation Koopman operator. Furthermore, according to the recovered and transmitted signals, the resilient formation controller with a variable gain takes into account the convergence rate and the steady state formation error. The Lyapunov theorem is introduced to prove that the formation error quickly converges to the minor compact set. A distributed DoS attack example is conducted to validate the efficiency and superiority in numerical simulation, and the proposed method is implemented on the real networked NMRs.  © 2024 IEEE.

Keyword:

Denial-of-service (DoS) attacks event-triggered mechanism (ETM) Koopman operator networked nonholonomic mobile robots (NMRs) resilient formation control

Community:

  • [ 1 ] [Zhan W.]Hunan University, College of Electrical and Information Engineering, National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha, 410082, China
  • [ 2 ] [Miao Z.]Hunan University, College of Electrical and Information Engineering, National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha, 410082, China
  • [ 3 ] [Zhang H.]Hunan University, School of Robotics, National Engineering Research Center for Robot Visual Perception and Control, Changsha, 410082, China
  • [ 4 ] [Chen Y.]Fuzhou University, School of Mechanical Engineering and Automation, Fuzhou, 350108, China
  • [ 5 ] [Chen Y.]Hunan University, National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha, 410082, China
  • [ 6 ] [Wu Z.-G.]Zhejiang University, Institute of Cyber-Systems and Control, Hangzhou, 310027, China
  • [ 7 ] [He W.]University of Science and Technology Beijing, School of Intelligence Science and Technology, Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, Beijing, 100083, China
  • [ 8 ] [Wang Y.]Hunan University, College of Electrical and Information Engineering, National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha, 410082, China

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Source :

IEEE Transactions on Systems, Man, and Cybernetics: Systems

ISSN: 2168-2216

Year: 2024

Issue: 11

Volume: 54

Page: 7065-7078

8 . 6 0 0

JCR@2023

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 0

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