Fuzzy Adaptive Learning Secure Control for Nonstrict-Pure-Feedback Cyber-Physical Systems Subject to Malicious Attacks

In this paper, fuzzy adaptive learning secure control (FALSC) strategy against uncertain malicious attacks is proposed for a class of nonlinear cyber-physical systems (CPSs) in the nonstrict-pure-feedback form. By means of appropriate system transformations and integral Lyapunov functions, the intra...

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Published inIEEE transactions on industrial cyber-physical systems Vol. 2; pp. 626 - 638
Main Authors Zou, Shengxiang, Sun, Mingxuan, Zhong, Guoming, He, Xiongxiong
Format Journal Article
LanguageEnglish
Published IEEE 2024
Subjects
Actuators
Adaptive learning
Adaptive learning control
adaptive resilient control
Adaptive systems
Backstepping
Complexity theory
Control design
Cyber-physical systems
deception attacks
Feedback
incremental adaptation
Lyapunov methods
nonstrict-pure-feedback
Performance analysis
Sensors
Uncertainty
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Abstract In this paper, fuzzy adaptive learning secure control (FALSC) strategy against uncertain malicious attacks is proposed for a class of nonlinear cyber-physical systems (CPSs) in the nonstrict-pure-feedback form. By means of appropriate system transformations and integral Lyapunov functions, the intractable problems associated with nonstrict-feedback and nonaffine structures are tackled, and the FALSC scheme presented is applicable to CPSs with more general system dynamics. Additionally, by the helpful property of basis function of the adopted fuzzy logic system, an improved backstepping based adaptive resilient control design is developed. As a result, the challenges caused by sensor and actuator deception attacks, including the original system information becoming unavailable and involving unknown time-varying attack gains, are overcome. Furthermore, an incremental adaptive mechanism is exploited and proved efficient in the treatment of unknown nonlinearities. The theoretical results about the tracking performance is established, where the boundedness of the signals in the closed-loop system is examined, and the robust stabilization of the system output under malicious attacks is characterized. Numerical results are provided to illustrate effectiveness of the proposed FALSC scheme.
AbstractList In this paper, fuzzy adaptive learning secure control (FALSC) strategy against uncertain malicious attacks is proposed for a class of nonlinear cyber-physical systems (CPSs) in the nonstrict-pure-feedback form. By means of appropriate system transformations and integral Lyapunov functions, the intractable problems associated with nonstrict-feedback and nonaffine structures are tackled, and the FALSC scheme presented is applicable to CPSs with more general system dynamics. Additionally, by the helpful property of basis function of the adopted fuzzy logic system, an improved backstepping based adaptive resilient control design is developed. As a result, the challenges caused by sensor and actuator deception attacks, including the original system information becoming unavailable and involving unknown time-varying attack gains, are overcome. Furthermore, an incremental adaptive mechanism is exploited and proved efficient in the treatment of unknown nonlinearities. The theoretical results about the tracking performance is established, where the boundedness of the signals in the closed-loop system is examined, and the robust stabilization of the system output under malicious attacks is characterized. Numerical results are provided to illustrate effectiveness of the proposed FALSC scheme.
Author Sun, Mingxuan
He, Xiongxiong
Zou, Shengxiang
Zhong, Guoming
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Snippet In this paper, fuzzy adaptive learning secure control (FALSC) strategy against uncertain malicious attacks is proposed for a class of nonlinear cyber-physical...
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SubjectTerms Actuators
Adaptive learning
Adaptive learning control
adaptive resilient control
Adaptive systems
Backstepping
Complexity theory
Control design
Cyber-physical systems
deception attacks
Feedback
incremental adaptation
Lyapunov methods
nonstrict-pure-feedback
Performance analysis
Sensors
Uncertainty
Title Fuzzy Adaptive Learning Secure Control for Nonstrict-Pure-Feedback Cyber-Physical Systems Subject to Malicious Attacks
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