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

Adaptive 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; Tracki...

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Main Authors: Hannan M.A., Ali J.A., Mohamed A., Uddin M.N.
Other Authors: 7103014445
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-232752023-05-29T14:39:01Z A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive Hannan M.A. Ali J.A. Mohamed A. Uddin M.N. 7103014445 56540826800 57195440511 55663372800 Adaptive 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 modulation This 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. Final 2023-05-29T06:39:01Z 2023-05-29T06:39:01Z 2017 Article 10.1109/TIE.2016.2631121 2-s2.0-85015323262 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015323262&doi=10.1109%2fTIE.2016.2631121&partnerID=40&md5=9da50b0f742fb3fc5af2ff2fa75af032 https://irepository.uniten.edu.my/handle/123456789/23275 64 4 7750547 2689 2699 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Adaptive 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 modulation
author2 7103014445
author_facet 7103014445
Hannan M.A.
Ali J.A.
Mohamed A.
Uddin M.N.
format Article
author Hannan M.A.
Ali J.A.
Mohamed A.
Uddin M.N.
spellingShingle Hannan M.A.
Ali J.A.
Mohamed A.
Uddin M.N.
A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive
author_sort Hannan M.A.
title A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive
title_short A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive
title_full A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive
title_fullStr A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive
title_full_unstemmed A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive
title_sort random forest regression based space vector pwm inverter controller for the induction motor drive
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806425827238215680
score 13.214268