Resilient Formation Control With Koopman Operator for Networked NMRs Under Denial-of-Service Attacks

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.

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Publication Details

Journal
IEEE Transactions on Systems Man and Cybernetics Systems
Published
2024-08-29
DOI
https://doi.org/10.1109/tsmc.2024.3445109
Citations
5
Primary Topic
Advanced NMR Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.20

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article

Resilient Formation Control With Koopman Operator for Networked NMRs Under Denial-of-Service Attacks

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5 citations
IEEE Transactions on Systems Man and Cybernetics Systems
Advanced NMR Techniques and Applications
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article

Resilient Formation Control With Koopman Operator for Networked NMRs Under Denial-of-Service Attacks

Zhiqiang Miao, Wei He, Yaonan Wang, Yanjie Chen, Zheng‐Guang Wu, Weiwei Zhan, Hui Zhang
article en
5 citations

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.

IEEE Transactions on Systems Man and Cybernetics SystemsVol. 54(11)
Hunan University (CN), Zhejiang University (CN), Fuzhou University (CN), University of Science and Technology Beijing (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 24%
Advanced NMR Techniques and Applications
1.20
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