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Fuzzy Tracking Control for Discrete-Time Nonlinear Network Systems with Privacy Protection and Dynamic Quantization

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Abstract

This paper investigates the problem of \({\mathcal {H}}_{\infty }\) performance output feedback tracking control of a nonlinear network control system with quantization effects in a privacy protection state. The nonlinear network system under study are represented by a Takagi–Sugeno fuzzy model. Dynamic quantization of the reference model and controlled object output signals are performed to reduce the load on the communication network. A function that converges gradually to the true value over time is used to protect the privacy of the reference model from eavesdroppers, and a tracking controller is designed to ensure that the system is asymptotically stable and has a given \({\mathcal {H}}_{\infty }\) performance. The sufficient conditions for the tracking controller are given in the form of linear matrix inequalities. Finally, the validity of the proposed method is verified using a nonlinear mass-spring-damped system.

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Funding

The work was supported in part by the Liaoning BaiQianWan Talents Program of China under Grant 2018049, the Joint Project of Key Laboratory of Liaoning Province of China under Grant 2019-KF-03-12 and the Science and Technology Research Project of Liaoning Provincial Education Department of China under Grant LJKZ1032.

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Authors and Affiliations

  1. College of Control Science and Engineering, Bohai University, Jinzhou, 121013, Liaoning, China

    Mingquan Li & Xiaoheng Chang

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  1. Mingquan Li
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Correspondence to Xiaoheng Chang.

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Li, M., Chang, X. Fuzzy Tracking Control for Discrete-Time Nonlinear Network Systems with Privacy Protection and Dynamic Quantization. Int. J. Fuzzy Syst. 25, 1227–1238 (2023). https://doi.org/10.1007/s40815-022-01436-3

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