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Energies
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Control and Monitoring of Permanent Magnet Synchronous Machines
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Control and Monitoring of Permanent Magnet Synchronous Machines

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (1 September 2020) | Viewed by 4846

Special Issue Editor

Prof. Dr. David Díaz Reigosa

E-Mail Website
Guest Editor
Department of Electrical, Computer and System Engineering, University of Oviedo, Gijón, Spain
Interests: control of electric machines; monitoring of electric machines; sensorless control of electric machines; fault diagnosis of electric machines; control of AC converters; microgrids; smart grids; distributed generation

Special Issue Information

Dear Colleagues,

Design and control of permanent magnet synchronous machines (PMSMs) have been the focus of significant research efforts during the last three decades due to their good dynamic performance, power density and efficiency. PMSMs are widely used in a large variety of applications, such as industrial applications, electric/hybrid vehicles, electric aircraft, and renewable power generation, due to their superior performance compared with other machines like induction or DC machines.

This Special Issue on "Control and Monitoring of Permanent Magnet Synchronous Machines" will focus on the latest advancements and future perspectives on control and monitoring of PMSMs.

Original technical papers are solicited on any subject pertaining to the scope of the Special Issue that includes, but is not limited to, the following topics:

  • Control techniques for PMSMs
  • Impact of machine design on PMSMs control performance
  • Implementation issues of PMSMs control for real products
  • Emerging control techniques for PMSMs
  • States estimation for motion control of PMSMs
  • Condition monitoring, fault diagnosis, and prognosis
  • Sensorless control of PMSM
  • Self-commissioning
  • Machine design for sensorless control
  • Thermal, materials, and efficiency Issues in PMSMs

We would be delighted to have your contribution to this Special Issue.

Prof. Dr. David Díaz Reigosa
Guest Editor

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. Energies is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • permanent magnet synchronous machines
  • motion control
  • monitoring techniques
  • fault diagnosis
  • sensorless techniques

Published Papers (2 papers)

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Research

25 pages, 8190 KiB  
Article
Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO
by Der-Fa Chen, Yi-Cheng Shih, Shih-Cheng Li, Chin-Tung Chen and Jung-Chu Ting
Energies 2020, 13(11), 2914; https://doi.org/10.3390/en13112914 - 06 Jun 2020
Cited by 2 | Viewed by 1636
Abstract
Because permanent-magnet synchronous linear motors (SLM) still exhibit nonlinear friction, ending effects and time-varying dynamic uncertainties, better control performances cannot be achieved by using common linear controllers. We propose a backstepping approach with three adaptive laws and a beating function to control the [...] Read more.
Because permanent-magnet synchronous linear motors (SLM) still exhibit nonlinear friction, ending effects and time-varying dynamic uncertainties, better control performances cannot be achieved by using common linear controllers. We propose a backstepping approach with three adaptive laws and a beating function to control the motion of permanent-magnet SLM drive systems that enhance the robustness of the system. In order to reduce greater vibration in situations with uncertainty actions in the aforementioned control systems, we propose an adaptive modified recurrent Rogers–Szego polynomials neural network backstepping (AMRRSPNNB) control system with three adaptive laws and reimbursed controller with decorated gray wolf optimization (DGWO), in order to handle external bunched force uncertainty, including nonlinear friction, ending effects and time-varying dynamic uncertainties, as well as to reimburse the minimal rebuild error of the reckoned law. In accordance with the Lyapunov stability, online parameter training method of the modified recurrent Rogers–Szego polynomials neural network (MRRSPNN) can be derived by utilizing an adaptive law. Furthermore, to help reduce error and better obtain learning fulfillment, the DGWO algorithm was used to change the two learning rates in the weights of the MRRSPNN. Finally, the usefulness of the proposed control system is validated by tested results. Full article
(This article belongs to the Special Issue Control and Monitoring of Permanent Magnet Synchronous Machines)
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16 pages, 7105 KiB  
Article
Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification
by Lihui Wang, Guojun Tan and Jie Meng
Energies 2019, 12(24), 4803; https://doi.org/10.3390/en12244803 - 17 Dec 2019
Cited by 16 | Viewed by 2800
Abstract
This paper reports the optimal control problem on the interior permanent magnet synchronous motor (IPMSM) systems. The control performance of the traditional model predictive control (MPC) controller is ruined due to the parameter uncertainty and mismatching. In order to solve the problem that [...] Read more.
This paper reports the optimal control problem on the interior permanent magnet synchronous motor (IPMSM) systems. The control performance of the traditional model predictive control (MPC) controller is ruined due to the parameter uncertainty and mismatching. In order to solve the problem that the MPC algorithm has a large dependence on system parameters, a method which integrates MPC control method and parameter identification for IPMSM is proposed. In this method, the d-q axis inductances and rotor permanent magnet flux of IPMSM motor are identified by the Adaline neural network algorithm, and then, the identification results are applied to the predictive controller and maximum torque per ampere (MTPA) module. The experimental results show that the optimized MPC control proposed in this paper has a good steady state and robust performance. Full article
(This article belongs to the Special Issue Control and Monitoring of Permanent Magnet Synchronous Machines)
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