Adaptive controller with fuzzy rules emulated structure and its applications

In this paper, the adaptive controller inspired by the neuro-fuzzy controller is proposed. Its structure, called fuzzy rules emulated network (FREN), is derived based on the fuzzy if-then rules. This structure not only emulates the fuzzy control rules but also allows the initial value of controller&...

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Main Authors: Treesatayapun C., Uatrongjit S.
格式: Article
語言:English
出版: 2014
在線閱讀:http://www.scopus.com/inward/record.url?eid=2-s2.0-18144378452&partnerID=40&md5=55ab8b7f93665d1ca75164dc3f8b655a
http://cmuir.cmu.ac.th/handle/6653943832/1279
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機構: Chiang Mai University
語言: English
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spelling th-cmuir.6653943832-12792014-08-29T09:29:03Z Adaptive controller with fuzzy rules emulated structure and its applications Treesatayapun C. Uatrongjit S. In this paper, the adaptive controller inspired by the neuro-fuzzy controller is proposed. Its structure, called fuzzy rules emulated network (FREN), is derived based on the fuzzy if-then rules. This structure not only emulates the fuzzy control rules but also allows the initial value of controller's parameters to be intuitively chosen. These parameters are further adjusted during system operation using a method similar to the steepest descent technique. The learning rate selection criteria based on Lyapunov's stability condition is also presented. FREN controller is applied to control various nonlinear systems, for examples, the single invert pendulum plant, the water bath temperature control, the high voltage direct current transmission system and the robotic system. Computer simulations results indicate that the proposed controller is able to control the target systems satisfactory. © 2005 Elsevier Ltd. All rights reserved. 2014-08-29T09:29:03Z 2014-08-29T09:29:03Z 2005 Article 09521976 10.1016/j.engappai.2004.12.006 EAAIE http://www.scopus.com/inward/record.url?eid=2-s2.0-18144378452&partnerID=40&md5=55ab8b7f93665d1ca75164dc3f8b655a http://cmuir.cmu.ac.th/handle/6653943832/1279 English
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description In this paper, the adaptive controller inspired by the neuro-fuzzy controller is proposed. Its structure, called fuzzy rules emulated network (FREN), is derived based on the fuzzy if-then rules. This structure not only emulates the fuzzy control rules but also allows the initial value of controller's parameters to be intuitively chosen. These parameters are further adjusted during system operation using a method similar to the steepest descent technique. The learning rate selection criteria based on Lyapunov's stability condition is also presented. FREN controller is applied to control various nonlinear systems, for examples, the single invert pendulum plant, the water bath temperature control, the high voltage direct current transmission system and the robotic system. Computer simulations results indicate that the proposed controller is able to control the target systems satisfactory. © 2005 Elsevier Ltd. All rights reserved.
format Article
author Treesatayapun C.
Uatrongjit S.
spellingShingle Treesatayapun C.
Uatrongjit S.
Adaptive controller with fuzzy rules emulated structure and its applications
author_facet Treesatayapun C.
Uatrongjit S.
author_sort Treesatayapun C.
title Adaptive controller with fuzzy rules emulated structure and its applications
title_short Adaptive controller with fuzzy rules emulated structure and its applications
title_full Adaptive controller with fuzzy rules emulated structure and its applications
title_fullStr Adaptive controller with fuzzy rules emulated structure and its applications
title_full_unstemmed Adaptive controller with fuzzy rules emulated structure and its applications
title_sort adaptive controller with fuzzy rules emulated structure and its applications
publishDate 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-18144378452&partnerID=40&md5=55ab8b7f93665d1ca75164dc3f8b655a
http://cmuir.cmu.ac.th/handle/6653943832/1279
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