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Neural Computing and Applications

Erschienen in:

01.12.2013 | Original Article

Nonlinear control of benchmark problems using TSK-type fuzzy neural network

verfasst von: Ching-Hung Lee, Wei-Yu Lai

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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Abstract

This paper proposes a TSK-type fuzzy neural network system (TFNN) for identifying and controlling nonlinear control benchmark problem system. It is available for nonlinear dynamic system with uncertainties. The TFNN system can construct and learn its knowledge base from the input–output training data firstly. Thus, a nonlinear system can be represented by several if-then rules with Gaussian membership functions and TSK-type consequent parts. Based on the learned TFNN system, a robust fuzzy controller is proposed, which combines linear matrix inequality-based fuzzy controller and fuzzy sliding model controller. Rigorous proof of asymptotic stability for the closed-loop system is presented via Lyapunov stability theorem. Several examples are presented to illustrate the effectiveness of our approach.

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Titel: Nonlinear control of benchmark problems using TSK-type fuzzy neural network
verfasst von: Ching-Hung Lee
Wei-Yu Lai
Publikationsdatum: 01.12.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1250-5

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