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Advanced Estimation, Control, and Optimization Techniques for Synchronous Machines in...
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Advanced Estimation, Control, and Optimization Techniques for Synchronous Machines in Next-Generation Electrified Systems

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1922

Special Issue Editors

Dr. Peter Azer

E-Mail Website
Guest Editor
1. Stellantis–Fiat Chrysler Automobiles (FCA), Motor Control Department, Hamilton, ON, Canada
2. Department of Electrical and Computer Engineering, McMaster University, ON L8S 4L8, Canada
Interests: synchronous motors; motor control; power electronics
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed Abdelrahman

E-Mail Website
Guest Editor
Professional Engineers Ontario & SimuTech Group, Niagara Falls, ON, Canada
Interests: electric machines design; thermal and CFD analyses of electric motors; electric drive EV/HEV traction systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synchronous machines—including permanent magnet synchronous machines (PMSMs), synchronous reluctance machines (SynRMs), and their variants—are at the core of high-performance electrified systems used in transportation, automation, and renewable energy. As the electrification of mobility and industry accelerates, demands are increasing for high-efficiency operations, sensorless control, fault tolerance, and robust real-time estimation strategies.

This Special Issue invites original research and comprehensive review papers focusing on the latest advances in modeling, estimation, and control of synchronous machines. We welcome contributions that address emerging trends, challenges, and opportunities in both theoretical and applied aspects of three-phase or multi-phase synchronous machine systems.

Topics of interest include, but are not limited to, the following:

  • Sensorless control techniques for PMSMs and SynRMs;
  • Real-time parameter estimation and adaptive observers;
  • Digital twin and AI-enhanced modeling of synchronous machines;
  • Flux and torque estimation under magnetic nonlinearity and saturation;
  • Current reconstruction and DC-link current estimation methods;
  • Field weakening and high-speed control of interior PMSMs;
  • Model predictive control and advanced FOC for synchronous drives;
  • Thermal modeling, derating strategies, and lifetime estimation;
  • Fault diagnosis and fault-tolerant control in drive systems;
  • Multi-objective optimization for efficiency, torque ripple, and noise.

We look forward to your contributions.

Dr. Peter Azer
Dr. Ahmed Abdelrahman
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • synchronous motors
  • motor control
  • power electronics
  • electric machines
  • synchronous machines
  • PMSM

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Published Papers (3 papers)

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Research

29 pages, 5362 KB  
Article
Multi-Objective Design Optimization of a MW Machine Using Hybrid Evolutionary Algorithm and Artificial Neural Networks
by Srikanth Pillai, Islam Zaher, Mohamed Abdalmagid and Ali Emadi
Machines 2026, 14(4), 408; https://doi.org/10.3390/machines14040408 - 8 Apr 2026
Viewed by 414
Abstract
In the aviation sector, there is a growing demand for high-specific-power electrical machines to realize More Electric Aircraft (MEA). The goals for these machines were set by the National Aeronautics and Space Administration (NASA) as 1 MW power, >13 kW kg−1 [...] Read more.
In the aviation sector, there is a growing demand for high-specific-power electrical machines to realize More Electric Aircraft (MEA). The goals for these machines were set by the National Aeronautics and Space Administration (NASA) as 1 MW power, >13 kW kg−1 of power density, and efficiency >96%. To address these requirements, this paper proposes an electromagnetic design of a high-speed, power-dense, 1 MW radial-flux Permanent Magnet Synchronous Machine (PMSM) for aerospace propulsion applications that achieves NASA targets. Achieving high-specific-power objectives necessitates geometry optimization that simultaneously minimizes motor mass while maximizing output power. This paper presents a faster optimization algorithm that hybridizes Genetic Algorithm and Artificial Neural Network (ANN)-based surrogate modeling to optimize the motor for multi-objective goals. The proposed framework employs a multi-objective approach targeting maximum torque output and efficiency within a minimum motor mass. This approach, using an ANN-based surrogate, significantly reduces optimization time by saving 95% of the time compared to FEM simulations. The optimized 1 MW motor attains 98% efficiency and an active power density of 24.87 kW kg−1. The various stages of the optimization are presented in detail and a comparison of the time saving using the proposed algorithm is outlined. To demonstrate the feasibility of design, a detailed electromagnetic analysis, stator thermal analysis with a jet impingement design, and magnet demagnetization risk analysis were also presented. Full article
(This article belongs to the Special Issue Advanced Estimation, Control, and Optimization Techniques for Synchronous Machines in Next-Generation Electrified Systems)
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16 pages, 3004 KB  
Article
Sensorless Speed Control of PMSM in the Low-Speed Region Using a Runge–Kutta Model-Based Nonlinear Gradient Observer
by Adile Akpunar Bozkurt
Machines 2026, 14(4), 369; https://doi.org/10.3390/machines14040369 - 27 Mar 2026
Viewed by 321
Abstract
High-performance operation of permanent magnet synchronous motors (PMSMs) strongly depends on the reliable availability of rotor position and speed information. Although this information is commonly obtained using physical position sensors, such sensors increase system cost and structural complexity and may reduce long-term reliability, [...] Read more.
High-performance operation of permanent magnet synchronous motors (PMSMs) strongly depends on the reliable availability of rotor position and speed information. Although this information is commonly obtained using physical position sensors, such sensors increase system cost and structural complexity and may reduce long-term reliability, particularly in demanding operating environments. In this study, a model-based, discrete-time, nonlinear gradient observer is adapted for the sensorless estimation of rotor speed and position in PMSMs. The developed Runge–Kutta model-based gradient observer (RKGO) utilizes stator voltage inputs and measured stator currents within a mathematical motor model to estimate the system states. In contrast to conventional sensorless estimation approaches, the adopted observer framework exploits discretization-based gradient dynamics to enhance numerical robustness and convergence behavior under nonlinear operating conditions. The observer design specifically targets stable and accurate state estimation in discrete-time implementations, with a particular focus on low-speed operating conditions. The performance of the adapted method is experimentally evaluated under low-speed operating conditions, including transient and steady-state operation. Real-time implementation is carried out on a dSPACE DS1104 control platform, including loaded acceleration scenarios to assess practical robustness. In addition, a comparative analysis with the Extended Kalman Filter (EKF) and the Runge–Kutta Extended Kalman Filter (RKEKF) is conducted at 60 rad/s under identical experimental conditions. Experimental results show that the RKGO method achieves accurate steady-state speed and position estimation with acceptable transient performance. The findings demonstrate that RKGO can be considered a viable alternative for low-speed sensorless PMSM drive applications. Full article
(This article belongs to the Special Issue Advanced Estimation, Control, and Optimization Techniques for Synchronous Machines in Next-Generation Electrified Systems)
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13 pages, 2796 KB  
Article
Real-Time Implementation of Auto-Tuned PID Control in PMSM Drives
by Adile Akpunar Bozkurt
Machines 2026, 14(1), 100; https://doi.org/10.3390/machines14010100 - 15 Jan 2026
Cited by 1 | Viewed by 691
Abstract
Permanent magnet synchronous motors (PMSM) are widely favored in industry for their high efficiency, compact size, and robust performance. This study employs a model-based PID control approach for speed regulation of PMSM. In contrast to traditional PID approaches, this method addresses the inherent [...] Read more.
Permanent magnet synchronous motors (PMSM) are widely favored in industry for their high efficiency, compact size, and robust performance. This study employs a model-based PID control approach for speed regulation of PMSM. In contrast to traditional PID approaches, this method addresses the inherent nonlinearity of PMSM systems and tunes PID coefficients dynamically for fast multi-input and multi-output (MIMO) operations. Traditional PID controllers typically assume linear motor dynamics and determine a single set of coefficients, often through trial and error. However, the nonlinear dynamics of motor drives and variations in motor parameters often lead to instability, limiting the effectiveness of conventional PID controllers. The proposed auto-tuning PID controller adjusts its coefficients in real-time based on the system’s operational state. This method has been implemented in both simulation and experimental setups, with real-time execution facilitated by dSPACE DS1104. A comparative analysis with conventional PI control demonstrates the enhanced stability and adaptability of the proposed approach. Full article
(This article belongs to the Special Issue Advanced Estimation, Control, and Optimization Techniques for Synchronous Machines in Next-Generation Electrified Systems)
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