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Predictive Control for Takagi–Sugeno Fuzzy Large-Scale Networked Control Systems

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Abstract

In this paper, the issue of exponential stabilization and sampled-data controller design for Takagi–Sugeno fuzzy large-scale networked control systems is studied by using the reduction-based ordinary differential equation prediction method. For the problem that matrices cannot be multiplied directly during the process of designing the sampled-data controller in this paper, a matrix dimensional transformation method is proposed. Firstly, a type of two-sided mode-dependent loop-based Lyapunov–Krasovskii functional is constructed, which compensates for the large delay and makes fuller use of the information in sampled-data interval. Secondly, the proposed method is used to give the design scheme of an aperiodic sampled-data controller, and furthermore, an iterative algorithm to verify the effectiveness of the requested control gains is provided. Finally, two coupled vehicle pendulum systems and two-area interconnected power systems are applied to demonstrate the efficiency of the presented approach.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 62373178, 62273201; the Research Fund for the Taishan Scholar Project of Shandong Province of China under Grant tstp20230629.

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Authors and Affiliations

  1. School of Mathematical Sciences, Liaocheng University, Liaocheng, 252000, People’s Republic of China

    Xiaoxiao Guo, Jianwei Xia, Tianjiao Liu & Chengyuan Yan

  2. School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, 243002, People’s Republic of China

    Hao Shen

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  1. Xiaoxiao Guo
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Correspondence to Jianwei Xia.

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Guo, X., Xia, J., Shen, H. et al. Predictive Control for Takagi–Sugeno Fuzzy Large-Scale Networked Control Systems. Int. J. Fuzzy Syst. (2024). https://doi.org/10.1007/s40815-023-01636-5

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