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Machines
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Data-Driven Control of Permanent Magnet Synchronous Motor (PMSM) Drives
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Data-Driven Control of Permanent Magnet Synchronous Motor (PMSM) Drives

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 3631

Special Issue Editors

Dr. Catalin Arghir

E-Mail Website
Guest Editor
Department of Electrical Engineering, ETH Zürich, 8092 Zürich, Switzerland
Interests: control systems; power electronics; grid forming converters; microgrids; mechatronics; motion control; electrical machines
Dr. Paolo Gherardo Carlet

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Padova, 2 - 35122 Padova, Italy
Interests: motor drives; torque control; predictive control; synchronous motors; sensorless control; parameter identification; reluctance motors

Special Issue Information

Dear Colleagues,

We are excited to announce a new Special Issue in Machines, “Data-Driven Control of Permanent Magnet Synchronous Motor (PMSM) Drives”.

Permanent magnet synchronous motors (PMSMs) are being increasingly used in various applications, such as electric vehicles, wind turbines, robotics, and industrial automation. This Special Issue aims to provide an overview of the latest research and developments regarding the data-driven control technology of PMSM drives.

We seek original research papers covering a wide range of topics, including, but not limited to, the following:

  • Data-Driven Predictive Control: Optimization-based and Predictive Control strategies utilizing data-driven approaches for PMSM drives.
  • System Identification: Data-driven identification and parameter estimation methods for PMSM systems.
  • Adaptive and Learning Control: Adaptive control strategies and machine learning-based approaches utilizing real-time data.
  • Reliability and Lifecycle Optimization: Strategies to enhance system robustness, extend the operational lifespan, and improve the overall reliability of PMSM drives.
  • Efficiency and Performance Optimization: Data-driven methods aimed at enhancing the overall performance of PMSM systems.

We welcome submissions from researchers, engineers, and practitioners in academia, industry, and government. We look forward to receiving your contributions and making this Special Issue a success.

Dr. Catalin Arghir
Dr. Paolo Gherardo Carlet
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (1 paper)

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Research

17 pages, 9041 KiB  
Article
Modified Super-Twisting Algorithm-Based Model Reference Adaptive Observer for Sensorless Control of the Interior Permanent-Magnet Synchronous Motor in Electric Vehicles
by Aykut Bıçak and Ayetül Gelen
Machines 2023, 11(9), 871; https://doi.org/10.3390/machines11090871 - 29 Aug 2023
Cited by 1 | Viewed by 2377
Abstract
In this paper, the model reference adaptive system (MRAS) method has been employed to observe speed in sensorless field-oriented control (FOC) with flux weakening (FW) and maximum torque per ampere (MTPA) operations for the interior permanent-magnet synchronous motor (IPMSM). This paper focuses on [...] Read more.
In this paper, the model reference adaptive system (MRAS) method has been employed to observe speed in sensorless field-oriented control (FOC) with flux weakening (FW) and maximum torque per ampere (MTPA) operations for the interior permanent-magnet synchronous motor (IPMSM). This paper focuses on the modified MRAS observer, which is based on the sigmoid function as a switching function and also the adaptive sliding mode coefficient. The sliding mode strategies are employed for the adaptation mechanism instead of the PI controller. The conventional PI-MRAS causes oscillations in rotor speed. To solve this problem, the modified adaptive super-twisting algorithm (STA)-based MRAS method is proposed by utilizing the sigmoid function. The proposed modified MRAS is compared to conventional methods. Additionally, it is examined for performance against the fast terminal sliding mode (FTSM), which is applied to the MRAS as an adaptation mechanism in terms of sliding mode strategies. The modified STA-MRAS is explored under the ECE and EUDC (Extra Urban Driving Cycle) drive cycles for electric vehicle applications. Finally, the obtained results show the validity and capability of the proposed adaptive STA-MRAS in terms of speed tracking. Full article
(This article belongs to the Special Issue Data-Driven Control of Permanent Magnet Synchronous Motor (PMSM) Drives)
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