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Decomposition Based Fuzzy Model Predictive Control Approaches for Interconnected Nonlinear Systems

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Abstract

This paper proposes fuzzy model predictive control (FMPC) strategies for nonlinear interconnected systems based mainly on a system decomposition approach. First, the Takagi-Sugeno (TS) fuzzy model is formulated in such a way to describe the behavior of the nonlinear system. Based on that description, a fuzzy model predictive control is determined. The system under consideration is decomposed into several subsystems. For each subsystem, the main idea consists of the decomposition of the control action into two parts: The decentralized part contains the parameters of the subsystem and the centralized part contains the elements of other subsystems. According to such decomposition, two strategies are defined aiming to circumvent the problems caused by interconnection between subsystems. The feasibility and efficiency of the proposed method are illustrated through numerical examples.

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Authors and Affiliations

  1. Control and Energy Management Laboratory (CEM-Lab), National School of Engineering of Sfax, University of Sfax, P.B. 1173, Sfax, 3083, Tunisia

    Latifa Dalhoumi, Mohamed Chtourou & Mohamed Djemel

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  1. Latifa Dalhoumi
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  2. Mohamed Chtourou
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  3. Mohamed Djemel
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Correspondence to Latifa Dalhoumi.

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Recommended by Associate Editor Jyh-Horng Chou

Latifa Dalhoumi received the B.Eng. degree in electrical engineering from National School of Engineering of Sfax (ENIS), Tunisia in 2008, the M. Sc. degree in automatic and industrial computing from the same Institute, Tunisia in 2009. She is currently a Ph. D. candidate in Control and Energy Management Laboratory (CEM-Lab) in the Department of Electrical Engineering of National School of Engineers of Sfax, Tunisia.

Her research interests include control of complex systems, interconnected linear and nonlinear systems, fuzzy model predictive control, and formulation of fuzzy model predictive control of nonlinear interconnected systems.

Mohamed Chtourou received the B.Eng. degree in electrical engineering from National School of Engineers of Sfax, Tunisia in 1989, the M. Sc. degree in automatic control from National Institute of Applied Sciences of Toulouse, France in 1990, and the Ph.D. degree in process engineering from the National Polytechnic Institute Toulouse, France in 1993, and was a post-doctoral in automatic control in the National School of Engineers of Sfax, the Habilitation University, Tunisia till 2002. He is currently a professor in the Department of Electrical Engineering of National School of Engineers of Sfax, Tunisia. He is an author and co-author of more than thirty papers in international journals and of more than 50 papers published in national and international conferences.

His research interests include learning algorithms, artificial neural networks and their engineering applications, fuzzy systems, and intelligent control.

Mohamed Djemel received the B. Sc., M. Sc., and Ph.D. degrees in electrical engineering from the High School of Technical Sciences of Tunis (ESSTT), Tunisia in 1987 and 1989 and 1996, respectively, and was a post-doctoral in the National School of Engineers of Sfax, the Habilitation University, Tunisia till 2006. He joined the Tunisian University since 1990, where he held different positions involved in research and education. Currently, he is a professor of automatic control at the Department of Electrical Engineering of the National School of Engineering of Sfax. He is a member of several national and international conferences.

His research interests include order reduction and the stability, the control and the advanced control of the complex systems.

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Dalhoumi, L., Chtourou, M. & Djemel, M. Decomposition Based Fuzzy Model Predictive Control Approaches for Interconnected Nonlinear Systems. Int. J. Autom. Comput. 16, 369–388 (2019). https://doi.org/10.1007/s11633-016-1021-3

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  • DOI: https://doi.org/10.1007/s11633-016-1021-3

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