Fuzzy Small-Gain Approach for the Distributed Optimization of T-S Fuzzy Cyber-Physical Systems

Zhenghong Jin; Xingjian Sun; Zhengyan Qin; Choon Ki Ahn

doi:10.1109/TCYB.2022.3202576

Fuzzy Small-Gain Approach for the Distributed Optimization of T-S Fuzzy Cyber-Physical Systems

Zhenghong Jin, Xingjian Sun, Zhengyan Qin, Choon Ki Ahn

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

The distributed optimization problem (DOP) of Takagi-Sugeno (T-S) fuzzy cyber-physical systems is studied under the framework of weight-balanced graphs and quasistrongly connected characteristics. The objective is to drive the outputs of all agents to the optimal solution of a given global objective function regarded as the desired output, based on the partial information of the local objective functions. To this end, distributed optimal coordinators (DOCs) are used to generate optimal solutions of local objective functions that converge to the desired output, and fuzzy reference-tracking controllers are designed to ensure that all agents can track the optimal solutions. As novel technical results, two Lyapunov-based fuzzy input-to-state stability (ISS) small-gain theorems are proposed for the T-S fuzzy interconnected system. Thus, the overall closed-loop system is an interconnected system involving the modules of optimal coordinators and fuzzy tracking controllers with T-S fuzzy subsystems. The fuzzy ISS cyclic-small-gain theorem is applied to analyze the system stability. The DOP of T-S fuzzy cyber-physical systems is solved using the DOCs and fuzzy reference-tracking controllers through the fuzzy small-gain approach. A numerical example is presented to demonstrate the effectiveness and superiority of the proposed method.

Original language	English
Pages (from-to)	6491-6502
Number of pages	12
Journal	IEEE Transactions on Cybernetics
Volume	53
Issue number	10
DOIs	https://doi.org/10.1109/TCYB.2022.3202576
Publication status	Published - 2023 Oct 1

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant U1911401; in part by the U.S. National Science Foundation under Grant EPCN-1903781; in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449.

Publisher Copyright:
© 2013 IEEE.

Keywords

Distributed optimization
Takagi-Sugeno (T-S) fuzzy cyber-physical systems
fuzzy input-to-state stability (ISS) cyclic-small-gain theorem
fuzzy reference-tracking controllers

ASJC Scopus subject areas

Software
Information Systems
Human-Computer Interaction
Electrical and Electronic Engineering
Control and Systems Engineering
Computer Science Applications

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10.1109/TCYB.2022.3202576

Cite this

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abstract = "The distributed optimization problem (DOP) of Takagi-Sugeno (T-S) fuzzy cyber-physical systems is studied under the framework of weight-balanced graphs and quasistrongly connected characteristics. The objective is to drive the outputs of all agents to the optimal solution of a given global objective function regarded as the desired output, based on the partial information of the local objective functions. To this end, distributed optimal coordinators (DOCs) are used to generate optimal solutions of local objective functions that converge to the desired output, and fuzzy reference-tracking controllers are designed to ensure that all agents can track the optimal solutions. As novel technical results, two Lyapunov-based fuzzy input-to-state stability (ISS) small-gain theorems are proposed for the T-S fuzzy interconnected system. Thus, the overall closed-loop system is an interconnected system involving the modules of optimal coordinators and fuzzy tracking controllers with T-S fuzzy subsystems. The fuzzy ISS cyclic-small-gain theorem is applied to analyze the system stability. The DOP of T-S fuzzy cyber-physical systems is solved using the DOCs and fuzzy reference-tracking controllers through the fuzzy small-gain approach. A numerical example is presented to demonstrate the effectiveness and superiority of the proposed method.",

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author = "Zhenghong Jin and Xingjian Sun and Zhengyan Qin and Ahn, {Choon Ki}",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant U1911401; in part by the U.S. National Science Foundation under Grant EPCN-1903781; in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449. Publisher Copyright: {\textcopyright} 2013 IEEE.",

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Fuzzy Small-Gain Approach for the Distributed Optimization of T-S Fuzzy Cyber-Physical Systems

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Keywords

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