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

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dc.citedby7
dc.contributor.authorZahraoui Y.en_US
dc.contributor.authorZaihidee F.M.en_US
dc.contributor.authorKermadi M.en_US
dc.contributor.authorMekhilef S.en_US
dc.contributor.authorAlhamrouni I.en_US
dc.contributor.authorSeyedmahmoudian M.en_US
dc.contributor.authorStojcevski A.en_US
dc.contributor.authorid57223913703en_US
dc.contributor.authorid56346969400en_US
dc.contributor.authorid57160269100en_US
dc.contributor.authorid57928298500en_US
dc.contributor.authorid56382973200en_US
dc.contributor.authorid55575761400en_US
dc.contributor.authorid55884935900en_US
dc.date.accessioned2024-10-14T03:18:11Z
dc.date.available2024-10-14T03:18:11Z
dc.date.issued2023
dc.description.abstractAn 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4353
dc.identifier.doi10.3390/en16114353
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85161635724
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161635724&doi=10.3390%2fen16114353&partnerID=40&md5=dfc3074b46f17fb44335785bb886b511
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34156
dc.identifier.volume16
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleEnergies
dc.subjectartificial neural network
dc.subjectfeedback linearization
dc.subjectfractional-order sliding mode control
dc.subjectnonlinear disturbance observer
dc.subjectPMSM drive
dc.subjectreinforcement learning
dc.subjectControllers
dc.subjectErrors
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectNeural networks
dc.subjectParameter estimation
dc.subjectPermanent magnets
dc.subjectReinforcement learning
dc.subjectSliding mode control
dc.subjectSpeed control
dc.subjectSynchronous motors
dc.subjectTwo term control systems
dc.subjectDisturbance observer
dc.subjectFeedback linearisation
dc.subjectFractional order
dc.subjectFractional-order sliding mode control
dc.subjectNonlinear disturbance
dc.subjectNonlinear disturbance observer
dc.subjectPermanent Magnet Synchronous Motor
dc.subjectPermanent-magnet synchronous motor drives
dc.subjectReinforcement learnings
dc.subjectSliding-mode control
dc.subjectFeedback linearization
dc.titleOptimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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