Bilateral Cooperative Control of Nonlinear Multiagent Systems With State and Output Quantification

The fuzzy adaptive state and output quantization bilateral cooperative control problem for nonlinear multiagent systems (NMASs) is studied. Since the considered system is nonlinear, fuzzy logic system (FLS) is applied to approximate the unknown nonlinear function, and a fuzzy state observer is constructed because the state cannot be measured. A second-order command filter is used to solve the complex problem of calculating the time derivative of the virtual control function, and a uniform quantizer is used for fuzzy adaptive inversion design in the process of controller design. Ultimately, the effectiveness of the proposed control method is verified by a series of simulation experiments and research results.

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Institutions

Publication Details

Journal
IEEE Transactions on Cybernetics
Published
2025-03-18
DOI
https://doi.org/10.1109/tcyb.2025.3545144
Citations
9
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
10.93

Funders

Controls
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article

Bilateral Cooperative Control of Nonlinear Multiagent Systems With State and Output Quantification

Tong Wang, Michael Basin, Jinyong Yu, Zhihong Zhao
9 citations
IEEE Transactions on Cybernetics
Advanced Control Systems Optimization
10.93
article

Bilateral Cooperative Control of Nonlinear Multiagent Systems With State and Output Quantification

Tong Wang, Michael Basin, Jinyong Yu, Zhihong Zhao
article en
9 citations

Abstract

The fuzzy adaptive state and output quantization bilateral cooperative control problem for nonlinear multiagent systems (NMASs) is studied. Since the considered system is nonlinear, fuzzy logic system (FLS) is applied to approximate the unknown nonlinear function, and a fuzzy state observer is constructed because the state cannot be measured. A second-order command filter is used to solve the complex problem of calculating the time derivative of the virtual control function, and a uniform quantizer is used for fuzzy adaptive inversion design in the process of controller design. Ultimately, the effectiveness of the proposed control method is verified by a series of simulation experiments and research results.

IEEE Transactions on CyberneticsVol. 55(6)
Ningbo University of Technology (CN), Harbin Institute of Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Heilongjiang Province, National Key Research and Development Program of China
Openalex Percentile: Top 2%
Advanced Control Systems Optimization
10.93
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Bilateral Cooperative Control of Nonlinear Multiagent Systems With State and Output Quantification — Tong Wang, Michael Basin, et al. · IEEE Transactions on Cybernetics (2025) | TGRS Research Map | TGRS