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Design of the fuzzy multiobjective controller based on the eligibility method
Author(s) -
Myung HwanChun,
Bien Z. Zenn
Publication year - 2003
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10101
Subject(s) - reinforcement learning , computer science , convergence (economics) , markov decision process , fuzzy logic , control (management) , controller (irrigation) , mathematical optimization , relation (database) , artificial neural network , artificial intelligence , markov process , mathematics , data mining , statistics , agronomy , economics , biology , economic growth
A multiobjective control problem has been handled in many different ways such as fuzzy, neural network and reinforcement learning, etc. Among them, a reinforcement learning method solves a multiobjective control problem without any prior knowledge. In this article, a new reinforcement learning method for a multiobjective control problem is proposed in consideration of its convergence. The proposed method, in which objective eligibility is considered for handling multirewards, reformulates a multiobjective control problem in a form of a reinforcement learning problem under non‐Markov environment. Using a similar relation to eligibility, the proposed method dealt with the previous research results of eligibility and was implemented with the concept of a decoupled fuzzy sliding mode control (DFSMC). © 2003 Wiley Periodicals, Inc.