TY  - CONF
CY  - Singapore
AV  - none
N2  - 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.
N1  - cited By 1; Conference of 2011 IEEE 9th International Conference on Power Electronics and Drive Systems, PEDS 2011 ; Conference Date: 5 December 2011 Through 8 December 2011; Conference Code:88711
TI  - Hybrid-fuzzy controller optimization via semi-parallel GA for servomotor control
ID  - scholars1555
SP  - 51
KW  - Control modes; Controller optimization; Defuzzifiers; Feedback controller; Fuzzifications; Input and outputs; Integral controllers; Modified genetic algorithms; Performance comparison; Scale Factor; Servo motor control; SPOGA; Test runs
KW  -  Controllers; Optimization; Power electronics; Servomotors
KW  -  Genetic algorithms
Y1  - 2011///
SN  - 9781612849997
A1  - Saad, N.
A1  - Wahyunggoro, O.
A1  - Ibrahim, T.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857437011&doi=10.1109%2fPEDS.2011.6147223&partnerID=40&md5=6cbb5d87615c1378467ec37602c14120
EP  - 56
ER  -