Staff View: Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning

Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning

An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC...

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Main Authors:	Zahraoui Y., Zaihidee F.M., Kermadi M., Mekhilef S., Alhamrouni I., Seyedmahmoudian M., Stojcevski A.
Other Authors:	57223913703
Format:	Article
Published:	MDPI 2024
Subjects:	artificial neural network feedback linearization fractional-order sliding mode control nonlinear disturbance observer PMSM drive reinforcement learning Controllers Errors Learning algorithms Learning systems Neural networks Parameter estimation Permanent magnets Reinforcement learning Sliding mode control Speed control Synchronous motors Two term control systems Disturbance observer Feedback linearisation Fractional order Fractional-order sliding mode control Nonlinear disturbance Nonlinear disturbance observer Permanent Magnet Synchronous Motor Permanent-magnet synchronous motor drives Reinforcement learnings Sliding-mode control Feedback linearization
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spelling	my.uniten.dspace-341562024-10-14T11:18:12Z Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning Zahraoui Y. Zaihidee F.M. Kermadi M. Mekhilef S. Alhamrouni I. Seyedmahmoudian M. Stojcevski A. 57223913703 56346969400 57160269100 57928298500 56382973200 55575761400 55884935900 artificial neural network feedback linearization fractional-order sliding mode control nonlinear disturbance observer PMSM drive reinforcement learning Controllers Errors Learning algorithms Learning systems Neural networks Parameter estimation Permanent magnets Reinforcement learning Sliding mode control Speed control Synchronous motors Two term control systems Disturbance observer Feedback linearisation Fractional order Fractional-order sliding mode control Nonlinear disturbance Nonlinear disturbance observer Permanent Magnet Synchronous Motor Permanent-magnet synchronous motor drives Reinforcement learnings Sliding-mode control Feedback linearization An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations. � 2023 by the authors. Final 2024-10-14T03:18:11Z 2024-10-14T03:18:11Z 2023 Article 10.3390/en16114353 2-s2.0-85161635724 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161635724&doi=10.3390%2fen16114353&partnerID=40&md5=dfc3074b46f17fb44335785bb886b511 https://irepository.uniten.edu.my/handle/123456789/34156 16 11 4353 All Open Access Gold Open Access MDPI 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/
topic	artificial neural network feedback linearization fractional-order sliding mode control nonlinear disturbance observer PMSM drive reinforcement learning Controllers Errors Learning algorithms Learning systems Neural networks Parameter estimation Permanent magnets Reinforcement learning Sliding mode control Speed control Synchronous motors Two term control systems Disturbance observer Feedback linearisation Fractional order Fractional-order sliding mode control Nonlinear disturbance Nonlinear disturbance observer Permanent Magnet Synchronous Motor Permanent-magnet synchronous motor drives Reinforcement learnings Sliding-mode control Feedback linearization
spellingShingle	artificial neural network feedback linearization fractional-order sliding mode control nonlinear disturbance observer PMSM drive reinforcement learning Controllers Errors Learning algorithms Learning systems Neural networks Parameter estimation Permanent magnets Reinforcement learning Sliding mode control Speed control Synchronous motors Two term control systems Disturbance observer Feedback linearisation Fractional order Fractional-order sliding mode control Nonlinear disturbance Nonlinear disturbance observer Permanent Magnet Synchronous Motor Permanent-magnet synchronous motor drives Reinforcement learnings Sliding-mode control Feedback linearization Zahraoui Y. Zaihidee F.M. Kermadi M. Mekhilef S. Alhamrouni I. Seyedmahmoudian M. Stojcevski A. Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning
description	An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations. � 2023 by the authors.
author2	57223913703
author_facet	57223913703 Zahraoui Y. Zaihidee F.M. Kermadi M. Mekhilef S. Alhamrouni I. Seyedmahmoudian M. Stojcevski A.
format	Article
author	Zahraoui Y. Zaihidee F.M. Kermadi M. Mekhilef S. Alhamrouni I. Seyedmahmoudian M. Stojcevski A.
author_sort	Zahraoui Y.
title	Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning
title_short	Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning
title_full	Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning
title_fullStr	Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning
title_full_unstemmed	Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning
title_sort	optimal tuning of fractional order sliding mode controller for pmsm speed using neural network with reinforcement learning
publisher	MDPI
publishDate	2024
_version_	1814061107035242496
score	13.222552

Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning

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