Self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive

Fuzzy Logic Control (FLC) are widely used in high-performance motor drives applications especially as the speed controller due to its capability to handle non-linear uncertainties, independent of plant models and rule-based algorithm. However, FLC with constant parameters (CPFL) may experience perfo...

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Main Author:	Ismail, Muhamad Zamani
Format:	Thesis
Language:	English English
Published:	2022
Subjects:	T Technology (General) TJ Mechanical engineering and machinery
Online Access:	http://eprints.utem.edu.my/id/eprint/26923/1/Self-tuning%20fuzzy%20logic%20speed%20controller%20based%20on%20model%20reference%20adaptive%20control%20for%20induction%20motor%20drive.pdf http://eprints.utem.edu.my/id/eprint/26923/2/Self-tuning%20fuzzy%20logic%20speed%20controller%20based%20on%20model%20reference%20adaptive%20control%20for%20induction%20motor%20drive.pdf
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id	my-utem-ep.26923
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spelling	my-utem-ep.269232023-10-16T10:58:59Z Self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive 2022 Ismail, Muhamad Zamani T Technology (General) TJ Mechanical engineering and machinery Fuzzy Logic Control (FLC) are widely used in high-performance motor drives applications especially as the speed controller due to its capability to handle non-linear uncertainties, independent of plant models and rule-based algorithm. However, FLC with constant parameters (CPFL) may experience performance degradation when the system operates away from the design point and encounters parameter variation or load disturbance. Therefore, this project is purposed to design Self-Tuning Fuzzy Logic Controller with Model Reference Adaptive Control (ST-MRAC) for Induction Motor (IM) drives. The proposed self-tuning mechanism-based FLC (ST-MRAC) is able to adjust the input change of error and output scaling factor of the main speed FLC continuously. This process enhances the accuracy of the input universe of disclose and crisp output simultaneously. This research begins by examining the performance of CPFL based on the standard and simplified rules for induction motor drive. The standard CPFL for the speed controller comprises 5x5 matrix rules that are tuned to achieve the best performance. A simplified fuzzy rule technique is used to replace the 25 rules with the dominance 7 rules are applied in order to reduce the computational burden. Based on the simplified 7 rules results, the self-tuning mechanism is designed for this proposed controller and as a result, only 14 rules are used for the ST-MRAC. All simulations worked are executed by using Simulink and Fuzzy tools in MATLAB software. Finally, an experimental investigation is carried out to validate the simulation results with the help of the digital signal controller board dSPACE DS1103 based on the induction motor drives system. The effectiveness of the proposed controller is examined by conducting a comparative analysis between CPFL and ST-MRAC over wide range operations, either in forward and reverse conditions, load disturbance and inertia variations. Based on the results, ST-MRAC has shown superior performance in transient and steady-state conditions in terms of various performance measures such as overshoot, rise time, settling time and recovery time over wide speed range operation. Quantitative performance comparisons are also conducted based on IAE and ITEA index. In comparison to CPFL, the proposed ST-MRAC improved on average 41.3% and 14.5 % for IAE and ITEA respectively in all speed operations. 2022 Thesis http://eprints.utem.edu.my/id/eprint/26923/ http://eprints.utem.edu.my/id/eprint/26923/1/Self-tuning%20fuzzy%20logic%20speed%20controller%20based%20on%20model%20reference%20adaptive%20control%20for%20induction%20motor%20drive.pdf text en public http://eprints.utem.edu.my/id/eprint/26923/2/Self-tuning%20fuzzy%20logic%20speed%20controller%20based%20on%20model%20reference%20adaptive%20control%20for%20induction%20motor%20drive.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=122113 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electrical Engineering Talib, Hairul Nizam
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Talib, Hairul Nizam
topic	T Technology (General) TJ Mechanical engineering and machinery
spellingShingle	T Technology (General) TJ Mechanical engineering and machinery Ismail, Muhamad Zamani Self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive
description	Fuzzy Logic Control (FLC) are widely used in high-performance motor drives applications especially as the speed controller due to its capability to handle non-linear uncertainties, independent of plant models and rule-based algorithm. However, FLC with constant parameters (CPFL) may experience performance degradation when the system operates away from the design point and encounters parameter variation or load disturbance. Therefore, this project is purposed to design Self-Tuning Fuzzy Logic Controller with Model Reference Adaptive Control (ST-MRAC) for Induction Motor (IM) drives. The proposed self-tuning mechanism-based FLC (ST-MRAC) is able to adjust the input change of error and output scaling factor of the main speed FLC continuously. This process enhances the accuracy of the input universe of disclose and crisp output simultaneously. This research begins by examining the performance of CPFL based on the standard and simplified rules for induction motor drive. The standard CPFL for the speed controller comprises 5x5 matrix rules that are tuned to achieve the best performance. A simplified fuzzy rule technique is used to replace the 25 rules with the dominance 7 rules are applied in order to reduce the computational burden. Based on the simplified 7 rules results, the self-tuning mechanism is designed for this proposed controller and as a result, only 14 rules are used for the ST-MRAC. All simulations worked are executed by using Simulink and Fuzzy tools in MATLAB software. Finally, an experimental investigation is carried out to validate the simulation results with the help of the digital signal controller board dSPACE DS1103 based on the induction motor drives system. The effectiveness of the proposed controller is examined by conducting a comparative analysis between CPFL and ST-MRAC over wide range operations, either in forward and reverse conditions, load disturbance and inertia variations. Based on the results, ST-MRAC has shown superior performance in transient and steady-state conditions in terms of various performance measures such as overshoot, rise time, settling time and recovery time over wide speed range operation. Quantitative performance comparisons are also conducted based on IAE and ITEA index. In comparison to CPFL, the proposed ST-MRAC improved on average 41.3% and 14.5 % for IAE and ITEA respectively in all speed operations.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Ismail, Muhamad Zamani
author_facet	Ismail, Muhamad Zamani
author_sort	Ismail, Muhamad Zamani
title	Self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive
title_short	Self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive
title_full	Self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive
title_fullStr	Self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive
title_full_unstemmed	Self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive
title_sort	self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electrical Engineering
publishDate	2022
url	http://eprints.utem.edu.my/id/eprint/26923/1/Self-tuning%20fuzzy%20logic%20speed%20controller%20based%20on%20model%20reference%20adaptive%20control%20for%20induction%20motor%20drive.pdf http://eprints.utem.edu.my/id/eprint/26923/2/Self-tuning%20fuzzy%20logic%20speed%20controller%20based%20on%20model%20reference%20adaptive%20control%20for%20induction%20motor%20drive.pdf
_version_	1783728751761162240

Self-tuning fuzzy logic speed controller based on model reference adaptive control for induction motor drive

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