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| status | dateread | title | year | authors | DOI | ISBN | citekey | itemType | journal | volume | issue | pages | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| false | Fuzzy Control Systems: Past, Present and Future | 2019 |
|
10.1109/MCI.2018.2881644 | nguyenFuzzyControlSystems2019 | journalArticle | _IEEE Computational Intelligence Magazine_ | 14 | 1 | 56-68 |
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Tags:
#Analytical-models, #Fuzzy-control, #Fuzzy-logic, #Fuzzy-systems, #Linguistics, #Nonlinear-systems, #Stability-analysis, #Zadeh,-Lotfi
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[!Abstract]
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 model-free 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. .
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