Reinforcement fuzzy control using Ant Colony Optimization

Author

Juang, Chia-Feng ; Lu, Chun-Ming

Author_Institution

Dept. of Electr. Eng., Nat. Chung Hsing Univ., Taichung

fYear

2008

fDate

12-15 Oct. 2008

Firstpage

927

Lastpage

931

Abstract

This paper proposes the design of a fuzzy controller by ant colony optimization (ACO) incorporated with fuzzy-Q learning, called ACO-FQ, with reinforcements. For a fuzzy controller, we list all candidate consequent control actions of each fuzzy rule. Each candidate in the consequent part of a rule is assigned with a corresponding Q-value. Searching for the best one among all combinations is partially based on pheromone trail and partially based on Q-values. To verify the performance of ACO-FQ, reinforcement fuzzy control of water bath temperature control system is simulated.

Keywords

control system synthesis; fuzzy control; fuzzy set theory; learning (artificial intelligence); learning systems; optimisation; search problems; ant colony optimization; fuzzy controller design; fuzzy rule; fuzzy set theory; fuzzy-Q learning; pheromone trail; reinforcement learning; search problem; water bath temperature control system; Acceleration; Ant colony optimization; Fuzzy control; Fuzzy systems; Genetic algorithms; Learning; Legged locomotion; Temperature control; Traveling salesman problems; Ant colont optimization; fuzzy Q-learning; reinforcement fuzzy control;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on

Conference_Location

Singapore

ISSN

1062-922X

Print_ISBN

978-1-4244-2383-5

Electronic_ISBN

1062-922X

Type

conf

DOI

10.1109/ICSMC.2008.4811399

Filename

4811399