TY - JOUR
T1 - CSIMFS
T2 - An algorithm to tune fuzzy logic controllers
AU - Rodríguez-Zalapa, Omar
AU - Huerta-Ruelas, Jorge A.
AU - Rangel-Miranda, Domingo
AU - Morales-Sánchez, Eduardo
AU - Hernández-Zavala, Antonio
N1 - Publisher Copyright:
© 2017 - IOS Press and the authors. All rights reserved.
PY - 2017
Y1 - 2017
N2 - This work, presents a new tuning algorithm for type-I fuzzy logic controllers (FLCs), called Change Symmetry in Input Membership Functions (CSIMFS). The algorithm uses an iterative method that applies specific criteria to reduce mathematical operations during the tuning process to develop embedded FLCs. It uses one-input-one-output fuzzy inference system with five control rules and no more than two tuning parameters. The effectiveness of the proposed algorithm was validated experimentally using a real nonlinear pneumatic positioning system and verified with a simulation using a second order model. In both cases, CSIMFS algorithm exhibited a better performance compared with heuristically tuned FLCs. Moreover, a new combined performance index was defined and compared to traditional ITAE index offering better results, reducing the maximum overshoot and a faster convergence of settling time.
AB - This work, presents a new tuning algorithm for type-I fuzzy logic controllers (FLCs), called Change Symmetry in Input Membership Functions (CSIMFS). The algorithm uses an iterative method that applies specific criteria to reduce mathematical operations during the tuning process to develop embedded FLCs. It uses one-input-one-output fuzzy inference system with five control rules and no more than two tuning parameters. The effectiveness of the proposed algorithm was validated experimentally using a real nonlinear pneumatic positioning system and verified with a simulation using a second order model. In both cases, CSIMFS algorithm exhibited a better performance compared with heuristically tuned FLCs. Moreover, a new combined performance index was defined and compared to traditional ITAE index offering better results, reducing the maximum overshoot and a faster convergence of settling time.
KW - FLC tuning algorithm
KW - controller performance indexes
KW - fuzzy logic control
KW - nonlinear control
UR - http://www.scopus.com/inward/record.url?scp=85025587829&partnerID=8YFLogxK
U2 - 10.3233/JIFS-161402
DO - 10.3233/JIFS-161402
M3 - Artículo
SN - 1064-1246
VL - 33
SP - 679
EP - 691
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
IS - 2
ER -