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Machines
Special Issues
Advanced Technologies for Smart Motor Diagnosis and Control
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Advanced Technologies for Smart Motor Diagnosis and Control

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 409

Editor

Prof. Dr. Ragab A. El-Sehiemy

E-Mail Website
Guest Editor
1. Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
2. Electronics Engineering Department, Higher Insitute for Engineering and Technology at Manzalla, El Manzala, Egypt
Interests: electric machines; power electronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electric motors play a fundamental role in modern industry, supporting applications in manufacturing, transportation, energy systems, and automation. With increasing demands for energy efficiency, reliability, intelligent operation, motor diagnosis and control technologies have become a critical area of research. Recent advancements in artificial intelligence, machine learning, advanced sensing, and IoT connectivity have significantly accelerated progress in real-time monitoring, predictive fault detection, and smart control strategies.

This Special Issue aims to gather high-quality research that advances intelligent diagnosis, condition monitoring, and control of motor systems. The scope aligns closely with the focus of Machines, particularly regarding mechanical system health monitoring, intelligent control, and smart industrial technologies. We welcome studies that explore new methodologies, theoretical developments, system designs, and practical applications that contribute to improving motor performance, reliability, and energy efficiency.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

  • AI- and machine-learning-based motor fault diagnosis
  • Advanced sensing and signal processing
  • Predictive maintenance and reliability analysis
  • Intelligent and adaptive motor control
  • IoT- and cloud-enabled monitoring systems
  • Digital twins for motor health and performance

We look forward to receiving your valuable contributions.

Prof. Dr. Ragab A. El-Sehiemy
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 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-anonymized 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

33 pages, 10821 KB  
Article
Metaheuristic-Based PI Controller Tuning Using a Multi-Error ITAE Objective Function for FOC-Controlled PMSM Drives in Electric Vehicle Applications
by Ahmed Mashaly, Mohamed Elgohary and Ragab A. El-Sehiemy
Machines 2026, 14(9), 959; https://doi.org/10.3390/machines14090959 - 24 Aug 2026
Abstract
Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) [...] Read more.
Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article
(This article belongs to the Special Issue Advanced Technologies for Smart Motor Diagnosis and Control)
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