Fuzzy Control Systems: Past, Present and Future

More than 40 years after fuzzy logic control appeared as an effective tool to deal with complex processes, the research on fuzzy control systems has constantly evolved. Mamdani fuzzy control was originally introduced as a modelfree control approach based on expert?s experience and knowledge. Due to...

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Bibliographic Details
Published in:IEEE computational intelligence magazine 2019-02, Vol.14 (1), p.56-68
Main Authors: Nguyen, Anh-Tu, Taniguchi, Tadanari, Eciolaza, Luka, Campos, Victor, Palhares, Reinaldo, Sugeno, Michio
Format: Magazinearticle
Language:English
Subjects:
Analytical models
Automatic
Control systems
Engineering Sciences
Formulations
Fuzzy control
Fuzzy logic
Fuzzy systems
Linguistics
Lotfi
Mathematical analysis
Matrix methods
Nonlinear systems
Stability analysis
Zadeh
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Summary:More than 40 years after fuzzy logic control appeared as an effective tool to deal with complex processes, the research on fuzzy control systems has constantly evolved. Mamdani fuzzy control was originally introduced as a modelfree control approach based on expert?s experience and knowledge. Due to the lack of a systematic framework to study Mamdani fuzzy systems, we have witnessed growing interest in fuzzy model-based approaches with Takagi-Sugeno fuzzy systems and singleton-type fuzzy systems (also called piecewise multiaffine systems) over the past decades. This paper reviews the key features of the three above types of fuzzy systems. Through these features, we point out the historical rationale for each type of fuzzy systems and its current research mainstreams. However, the focus is put on fuzzy model-based approaches developed via Lyapunov stability theorem and linear matrix inequality (LMI) formulations. Finally, our personal viewpoint on the perspectives and challenges of the future fuzzy control research is discussed.
ISSN:1556-603X
1556-6048
1556-6048
1556-603X
DOI:10.1109/MCI.2018.2881644