TY - JOUR
T1 - A hybrid optimization method with PSO and GA to automatically design Type-1 and Type-2 fuzzy logic controllers
AU - Martínez-Soto, Ricardo
AU - Castillo, Oscar
AU - Aguilar, Luis T.
AU - Rodriguez, Antonio
N1 - Publisher Copyright:
© 2013, Springer-Verlag Berlin Heidelberg.
PY - 2015/4
Y1 - 2015/4
N2 - In this paper we propose the use of a hybrid PSO-GA optimization method for automatic design of fuzzy logic controllers (FLC) to minimize the steady state error of a plant’s response. We test the optimal FLC obtained by the hybrid PSO-GA method using benchmark control plants. The bio-inspired and the evolutionary methods are used to find the parameters of the membership functions of the FLC to obtain the optimal controller. Simulation results are obtained to show the feasibility of the proposed approach. A comparison is also made among the proposed Hybrid PSO-GA, GA and PSO to determine if there is a significant difference in the results.
AB - In this paper we propose the use of a hybrid PSO-GA optimization method for automatic design of fuzzy logic controllers (FLC) to minimize the steady state error of a plant’s response. We test the optimal FLC obtained by the hybrid PSO-GA method using benchmark control plants. The bio-inspired and the evolutionary methods are used to find the parameters of the membership functions of the FLC to obtain the optimal controller. Simulation results are obtained to show the feasibility of the proposed approach. A comparison is also made among the proposed Hybrid PSO-GA, GA and PSO to determine if there is a significant difference in the results.
KW - Fuzzy logic controllers
KW - Genetic algorithms
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=84924763348&partnerID=8YFLogxK
U2 - 10.1007/s13042-013-0170-8
DO - 10.1007/s13042-013-0170-8
M3 - Artículo
SN - 1868-8071
VL - 6
SP - 175
EP - 196
JO - International Journal of Machine Learning and Cybernetics
JF - International Journal of Machine Learning and Cybernetics
IS - 2
ER -