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

Ching Hung Lee*, Wei Yu Lai

*此作品的通信作者

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)83-94
頁數12
期刊Neural Computing and Applications
23
發行號SUPPL1
DOIs
出版狀態Published - 2013

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