Robust adaptive control for nonlinear cyber‐physical systems with FDI attacks via attack estimation

This article investigates the adaptive control problem for nonlinear cyber‐physical systems with network communication encountered false data injection (FDI) attacks. To address such attacks, the attack estimate method is designed whose objective is to minimize the vulnerability of FDI attacks. This...

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Published inInternational journal of robust and nonlinear control Vol. 33; no. 15; pp. 9299 - 9316
Main Authors Chen, Lexin, Li, Yongming, Tong, Shaocheng
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 01.10.2023
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Abstract This article investigates the adaptive control problem for nonlinear cyber‐physical systems with network communication encountered false data injection (FDI) attacks. To address such attacks, the attack estimate method is designed whose objective is to minimize the vulnerability of FDI attacks. This article aim to find, using the historical FDI attack, a solution with guaranteed out‐of‐sample forecasting, so as for the attacker to plan its attacks such that the worst possible action on the system measurement. The approach is to formulate a robust optimization problem using the box‐like sets, and then transform it into a linear programming model for solving problems. Consequently, under the framework of backstepping, a robust adaptive state‐feedback control method is proposed. By using Lyapunov stability theory, the proposed control scheme can guarantee that all the closed‐loop signals are globally bounded and the stabilization error converges to the origin. Finally, simulation results illustrate the effectiveness of the proposed control scheme.
AbstractList This article investigates the adaptive control problem for nonlinear cyber‐physical systems with network communication encountered false data injection (FDI) attacks. To address such attacks, the attack estimate method is designed whose objective is to minimize the vulnerability of FDI attacks. This article aim to find, using the historical FDI attack, a solution with guaranteed out‐of‐sample forecasting, so as for the attacker to plan its attacks such that the worst possible action on the system measurement. The approach is to formulate a robust optimization problem using the box‐like sets, and then transform it into a linear programming model for solving problems. Consequently, under the framework of backstepping, a robust adaptive state‐feedback control method is proposed. By using Lyapunov stability theory, the proposed control scheme can guarantee that all the closed‐loop signals are globally bounded and the stabilization error converges to the origin. Finally, simulation results illustrate the effectiveness of the proposed control scheme.
This article investigates the adaptive control problem for nonlinear cyber‐physical systems with network communication encountered false data injection (FDI) attacks. To address such attacks, the attack estimate method is designed whose objective is to minimize the vulnerability of FDI attacks. This article aim to find, using the historical FDI attack, a solution with guaranteed out‐of‐sample forecasting, so as for the attacker to plan its attacks such that the worst possible action on the system measurement. The approach is to formulate a robust optimization problem using the box‐like sets, and then transform it into a linear programming model for solving problems. Consequently, under the framework of backstepping, a robust adaptive state‐feedback control method is proposed. By using Lyapunov stability theory, the proposed control scheme can guarantee that all the closed‐loop signals are globally bounded and the stabilization error converges to the origin. Finally, simulation results illustrate the effectiveness of the proposed control scheme.
Author Li, Yongming
Tong, Shaocheng
Chen, Lexin
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SubjectTerms Adaptive control
Control methods
Feedback control
Linear programming
Nonlinear control
Optimization
Problem solving
Robust control
Title Robust adaptive control for nonlinear cyber‐physical systems with FDI attacks via attack estimation
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