Optimization of a Fuzzy PI Controller using Reinforcement Learning

This paper proposes a methodology for fine tuning of the conclusion part of fuzzy proportional-integral controllers (FPIC), using both a reinforcement learning method and all the available knowledge on the process under control. Membership functions on the error domain and rule conclusions are easil...

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Bibliographic Details
Main Authors: Boubertakh, H., Glorennec, P.-Y.
Format: Conference Proceeding
Language:English
Subjects:
Control systems
Fuzzy control
Fuzzy logic
Fuzzy PI Controller
Fuzzy sets
Gold
Learning
Process control
Proportional control
Reinforcement Learning
Three-term control
Zirconium
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Summary:This paper proposes a methodology for fine tuning of the conclusion part of fuzzy proportional-integral controllers (FPIC), using both a reinforcement learning method and all the available knowledge on the process under control. Membership functions on the error domain and rule conclusions are easily defined. Therefore only the conclusion part have to be tuned
DOI:10.1109/ICTTA.2006.1684633