Publication:
Quantum-Behaved Lightning Search Algorithm to Improve Indirect Field-Oriented Fuzzy-PI Control for im Drive

Simple item page
dc.citedby32
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorAli J.A.en_US
dc.contributor.authorMohamed A.en_US
dc.contributor.authorAmirulddin U.A.U.en_US
dc.contributor.authorTan N.M.L.en_US
dc.contributor.authorUddin M.N.en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid56540826800en_US
dc.contributor.authorid57195440511en_US
dc.contributor.authorid26422804600en_US
dc.contributor.authorid24537965000en_US
dc.contributor.authorid55663372800en_US
dc.date.accessioned2023-05-29T06:51:50Z
dc.date.available2023-05-29T06:51:50Z
dc.date.issued2018
dc.descriptionFuzzy logic; Induction motors; Learning algorithms; Mean square error; Membership functions; Optimization; Particle swarm optimization (PSO); Rotors; Speed; Speed control; Stators; Torque; Transient analysis; Vector control (Electric machinery); Voltage control; Water craft; Backtracking search algorithms; Fuzzy membership function; Gravitational search algorithm (GSA); Indirect field oriented control; PI Controller; QLSA; Speed controller; Three phase induction motor; Controllersen_US
dc.description.abstractThe main objective of this study is to develop a quantum-behaved lightening search algorithm (QLSA) to improve the indirect field-oriented fuzzy-proportional-integral (PI) controller technique to control a three-phase induction motor (TIM) drive. The generated adaptive PI current control parameters and fuzzy membership functions are carried to design induction motor drive speed controller to minimize the fitness function formulated by QLSA. An optimal QLSA-based indirect field-oriented control (QLSA-IFOC) fitness function is used to reduce the mean absolute error of the rotor speed to improve the performance of the TIM with varying speed and mechanical load. Results obtained from the QLSA-IFOC are compared with those obtained through lightening search algorithm, gravitational search algorithm, backtracking search algorithm, and particle swarm optimization to validate the developed controller. The optimization results of objective functions in terms of box plots and iterations show that the QLSA algorithm outperforms the other optimization algorithms. Moreover, the QLSA-IFOC controller performed well in all tests in terms of transient response. The developed controller also minimizes overshoot, increases damping capability, and reduces the root-mean-square error, as well as standard deviation under sudden change of speed and mechanical loads. A comparative analysis is performed between simulation and experimental results to justify the efficiency of the developed controller. � 1972-2012 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TIA.2018.2821644
dc.identifier.epage3805
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85044737049
dc.identifier.spage3793
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85044737049&doi=10.1109%2fTIA.2018.2821644&partnerID=40&md5=ab1007419047b2adc4e897a4b8f7ee0a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23788
dc.identifier.volume54
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Industry Applications
dc.titleQuantum-Behaved Lightning Search Algorithm to Improve Indirect Field-Oriented Fuzzy-PI Control for im Driveen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS