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A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive

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dc.citedby49
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorAli J.A.en_US
dc.contributor.authorMohamed A.en_US
dc.contributor.authorUddin M.N.en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid56540826800en_US
dc.contributor.authorid57195440511en_US
dc.contributor.authorid55663372800en_US
dc.date.accessioned2023-05-29T06:39:01Z
dc.date.available2023-05-29T06:39:01Z
dc.date.issued2017
dc.descriptionAdaptive control systems; Controllers; Decision trees; Deep neural networks; Electric drives; Electric inverters; Electric motors; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Induction motors; Inference engines; Learning algorithms; Modulation; Neural networks; Regression analysis; Tracking (position); Two term control systems; Vector spaces; Vectors; Voltage control; Adaptive neuro-fuzzy inference system; Backtracking search algorithms; Different operating conditions; Proportional integral controllers; Random forests; Space Vector Modulation; Space vector pulse width modulation; Three phase induction motor; Pulse width modulationen_US
dc.description.abstractThis paper presents a random forest (RF) regression based implementation of space vector pulse width modulation (SVPWM) for a two-level inverter to improve the performance of the three-phase induction motor (TIM) drive. The RF scheme offers the advantage of rapid implementation and improved prediction for the SVPWM algorithm to improve the performance of a conventional space vector modulation scheme. In order to show the superiority of the proposed RF technique to other techniques, an adaptive neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) based SVPWM schemes are also used and compared. The proposed speed controller uses a backtracking search algorithm to search for the best values for the proportional-integral controller parameters. The robustness of the RF-based SVPWM is found superior to the ANFIS and ANN controllers in all tested cases in terms of damping capability, settling time, steady-state error, and transient response under different operating conditions. The prototype of the optimal RF-based SVPWM inverter controller of induction motor drive is fabricated and tested. Several experimental results show that there is a good agreement of the speed response and stator current with the simulation results which are verified and validated the performance of the proposed RF-based SVPWM inverter controller. � 2016 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7750547
dc.identifier.doi10.1109/TIE.2016.2631121
dc.identifier.epage2699
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85015323262
dc.identifier.spage2689
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85015323262&doi=10.1109%2fTIE.2016.2631121&partnerID=40&md5=9da50b0f742fb3fc5af2ff2fa75af032
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23275
dc.identifier.volume64
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Industrial Electronics
dc.titleA Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Driveen_US
dc.typeArticleen_US
dspace.entity.typePublication
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