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AuthorMasoudi, Siamak
AuthorMehrjerdi, Mehrjerdi
Available date2022-11-14T10:49:07Z
Publication Date2022
Publication NameIEEE Transactions on Power Electronics
ResourceScopus
Resource2-s2.0-85122479638
URIhttp://dx.doi.org/10.1109/TPEL.2021.3125606
URIhttp://hdl.handle.net/10576/36300
AbstractSwitched reluctance motors are nonlinear systems with some uncertainties and unmodeled dynamics. Propulsion force and speed in these motors have inherently high fluctuations complicating their applications. The conventional controllers could not offer a precise performance for nonlinear systems because they require analytical calculations of the partial derivatives. Accordingly, in this article, a multilayer perception is presented to overcome this problem and control a linear motor. Training algorithms require a complete dataset of the system output, which complicates their implementation. To solve this problem, a Kalman filter is used to estimate uncertain parameters. Thus, the proposed control system does not require a complete dataset of the system. It can process data and predict the next values in a short time without complete observations. The proposed control strategy is implemented to a linear switched reluctance motor and the results are compared with two other conventional methods via simulation and experimental tests. The results confirm the ability and accuracy of the proposed method. 2012 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectLinear motors
Multilayers
Reluctance motors
Uncertainty analysis
Analytical calculation
Conventional controllers
Linear switched reluctance motor
Multi-layer perception
Partial derivatives
Performance
Propulsion force
Switched Reluctance Motor - SRM
Uncertainty
Unmodeled dynamics
Nonlinear systems
TitleA Multilayer Perception Trained Method in Speed Control of a Linear Switched Reluctance Motor
TypeArticle
Pagination4475-4483
Issue Number4
Volume Number37
dc.accessType Abstract Only


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