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Design issues for a reinforcement-based self-learning fuzzy controllerFuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.
Document ID
19930013173
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Yen, John
(Texas A&M Univ. College Station, TX, United States)
Wang, Haojin
(Texas A&M Univ. College Station, TX, United States)
Dauherity, Walter
(Texas A&M Univ. College Station, TX, United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1993
Publication Information
Publication: NASA. Johnson Space Center, Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, Volume 1
Subject Category
Cybernetics
Accession Number
93N22362
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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