relation: https://khub.utp.edu.my/scholars/1555/
title: Hybrid-fuzzy controller optimization via semi-parallel GA for servomotor control
creator: Saad, N.
creator: Wahyunggoro, O.
creator: Ibrahim, T.
description: Servomotor uses feedback controller to control either the speed or the position or both. This paper discusses the performance comparisons of a modified genetic algorithm, named as the semi-parallel operation genetic algorithm (SPOGA) and the conventional genetic algorithm (GA), in optimizing the I/O scale factors, membership functions, and rules of a hybrid-fuzzy controller. Singleton fuzzification is used as a fuzzifier with seven membership functions for both input and output of the controller, whilst center of average is used as a defuzzifier. A 21-bit-30-population is used in SPOGA for both I/O scales and for membership functions. Two control modes are applied in cascaded: position and speed. Both the simulation and practical experiment results show that fuzzy-logic parallel integral controller (FLIC) with SPOGA-optimized is better as compared to FLIC with GA-optimized and also the non-optimized FLIC, FLC, and PI in terms of performance and the reduction of the number of test runs for the optimization. © 2011 IEEE.
date: 2011
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Saad, N. and Wahyunggoro, O. and Ibrahim, T.  (2011) Hybrid-fuzzy controller optimization via semi-parallel GA for servomotor control.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857437011&doi=10.1109%2fPEDS.2011.6147223&partnerID=40&md5=6cbb5d87615c1378467ec37602c14120
relation: 10.1109/PEDS.2011.6147223
identifier: 10.1109/PEDS.2011.6147223