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Predictive sliding-mode control of networked high-order fully actuated systems under random deception attacks

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  • Special Topic: Control, Optimization, and Learning for Networked Systems
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Abstract

This research addresses the output tracking of networked high-order fully actuated (NHOFA) systems under random deception attacks in the sensor to networked controller (SNC) and networked controller to actuator (NCA) communication channels, where a Bernoulli process represents the launching success rate of random deception attacks. A successful attack involves the output and control signals being tampered with by the injection of false data. Herein, a predictive sliding-mode control scheme is proposed to achieve the security tracking. In this scheme, a sliding-mode variable is introduced to defend the random deception attacks by enhancing the robustness of closed-loop systems. An incremental HOFA (IHOFA) prediction model is developed using a Diophantine equation as an alternative to the traditional reduced-order prediction model. Through this IHOFA prediction model, the multistep ahead predictions of the sliding-mode variable are constructed to optimize the tracking performance and defense of random deception attacks. By utilizing the Lyapunov function and linear matrix inequality (LMI) approach, a sufficient and necessary condition is proposed to ensure stability and high tracking performance of closed-loop NHOFA systems. Herein, an experiment on the tracking control of an air-bearing spacecraft (ABS) simulator is performed to demonstrate the effectiveness and practicability of the predictive sliding-mode control scheme.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 62173255, 62188101) and Shenzhen Key Laboratory of Control Theory and Intelligent Systems (Grant No. ZDSYS20220330161800001).

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Correspondence to Guo-Ping Liu.

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Zhang, DW., Liu, GP. Predictive sliding-mode control of networked high-order fully actuated systems under random deception attacks. Sci. China Inf. Sci. 66, 190204 (2023). https://doi.org/10.1007/s11432-022-3817-5

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  • DOI: https://doi.org/10.1007/s11432-022-3817-5

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