Control Performance of Discrete-Time Fuzzy Systems Improved by Neural Networks

Chien-Hsing SU  Cheng-Sea HUANG  Kuang-Yow LIAN  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E89-A   No.5   pp.1446-1453
Publication Date: 2006/05/01
Online ISSN: 1745-1337
DOI: 10.1093/ietfec/e89-a.5.1446
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Systems and Control
T-S fuzzy systems,  fuzzy modeling,  discrete-time systems,  neural networks,  linear matrix inequalities,  

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A new control scheme is proposed to improve the system performance for discrete-time fuzzy systems by tuning control grade functions using neural networks. According to a systematic method of constructing the exact Takagi-Sugeno (T-S) fuzzy model, the system uncertainty is considered to affect the membership functions. Then, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of LMIs which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme applied to a truck-trailer system is verified by satisfactory simulation results.