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Research Article | Open Access

MIMO fuzzy adaptive control systems based on fuzzy semi-tensor product

Hongli Lyu1Yanan Lyu2( )Yongchao Gao3Heng Qian3Shan Du4
Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, O. N., Canada
Department of Fundamental Courses, Shandong University of Science and Technology, Tai'an, China
Shandong Computer Science Center (National Supercomputer Center in Ji'nan), Qilu University of Technology (Shandong Academy of Science), Ji'nan, China
Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science, University of British Columbia Okanagan, Kelowna, Canada
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Abstract

Based on fuzzy semi-tensor product (STP) algorithms and fuzzy relation matrix (FRM) models, the design of an adaptive fuzzy controller was proposed in this paper for the multivariable nonlinear systems with uncertainty. The controlled multi-input-and-multi-output (MIMO) plants were expressed and processed first by FRM models and fuzzy STP operations, and then the indirect adaptive fuzzy control laws were designed. The tracking property of the FRM models was proved for the control objective of MIMO systems. The effectiveness of the novel matrix expression was verified by simulations of the tracking control on a two-link rigid robot manipulator. Results in simulation tests show that the proposed design of adaptive FRM control is efficient for nonlinear multivariables. Therefore, the proposed indirect fuzzy adaptive controllers can be extended to general matrix expression for MIMO nonlinear systems with fuzzy STP algorithms and FRM models and online approximate unknown parameters, according to required accuracy.

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Mathematical Modelling and Control
Pages 316-330

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Cite this article:
Lyu H, Lyu Y, Gao Y, et al. MIMO fuzzy adaptive control systems based on fuzzy semi-tensor product. Mathematical Modelling and Control, 2023, 3(4): 316-330. https://doi.org/10.3934/mmc.2023026

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Received: 30 December 2022
Revised: 01 April 2023
Accepted: 25 April 2023
Published: 15 December 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)