NDLI: Improving the performance of multiple models fuzzy control by using semi-fixed and adaptive models

Content Provider	IEEE Xplore Digital Library
Author	Sofianos, N.A. Boutalis, Y.S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Democritus Univ. of Thrace, Xanthi, Greece (Sofianos, N.A.; Boutalis, Y.S.)
Abstract	The role of the fixed models that participate in a hybrid switching control scheme is investigated in this paper. The control scheme is based on some semi-fixed and adaptive Takagi-Sugeno (T-S) identification models and its main target is to control efficiently a class of unknown nonlinear dynamical fuzzy systems. These identification models define the control signal at every time instant with their own state feedback fuzzy controllers which are parameterized by using the certainty equivalence approach. A performance index and an appropriate switching rule are used to determine the T-S model that approximates the plant best and consequently to pick the best available controller at every time instant. The identification models bank consists of three kind of models: i. a number of semi-fixed T-S models which are redistributed during the control procedure, ii. a free adaptive T-S model which is randomly initialized and iii. a reinitialized adaptive model which uses the parameters of the best semi-fixed model at every time instant. The combination of these different model categories, offers many advantages to the control scheme. The asymptotic stability of the system and the adaptive laws for the adaptive models are given by using Lyapunov stability theory. The effectiveness and the advantages of the proposed method are illustrated by some computer simulations.
Starting Page	278
Ending Page	283
File Size	322406
Page Count	6
File Format	PDF
e-ISBN	9781479909971
DOI	10.1109/MED.2013.6608734
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-25
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Solid modeling Switches Stability analysis Mathematical model Equations
Content Type	Text
Resource Type	Article

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