https://ijcds.org/index.php/home/issue/feedInternational journal of Computing & Decision Sciences2026-02-19T16:28:47+00:00International Journal of Computing & Decision Sciences (IJCDS)editor@ijcds.orgOpen Journal Systems<p><sup><strong>International journal of computing & decision sciences (IJCDS)</strong> is an <strong>open access,</strong> <strong>peer-reviewed</strong> international journal. IJCDS releases quarterly ( Four issues in a year) issues. The Journal dedicated to publish high quality research articles in the fields of computing and decision sciences. </sup></p> <p><sup><strong>IMPACT FACTOR (2025): 7.8</strong></sup></p> <p><sup><strong>Aim & Scope</strong></sup></p> <ul> <li><sup>Approximate reasoning</sup></li> <li><sup>Artificial intelligence</sup></li> <li><sup>Combinatorial optimization</sup></li> <li><sup>Computational complexity theory</sup></li> <li><sup>Databases and data warehouses</sup></li> <li><sup>Intelligent decision support</sup></li> <li><sup>Knowledge engineering</sup></li> <li><sup>Machine learning and data mining</sup></li> <li><sup>Metaheuristics</sup></li> <li><sup>Multiple criteria decision analysis</sup></li> <li><sup>Networking and distributed systems</sup></li> <li><sup>Parallel computing and concurrency</sup></li> <li><sup>Production and project scheduling</sup></li> <li><sup>Scheduling theory</sup></li> <li><sup>Soft and granular computing</sup></li> <li><sup>Software engineering</sup></li> </ul> <p><sup>The journal also encourages the submission of useful reports of negative results. IJCDS dedicated to publish scope based high quality research & review articles in the field of computing & decision sciences. All manuscripts are pre-reviewed by the editorial review committee.</sup></p> <p><sup>Contributions must be original, not previously or simultaneously published elsewhere, and are critically reviewed before they are published. Papers, which must be written in English, should have sound grammar and proper terminologies.</sup></p> <p><sup>The published papers are made highly visible to the scientific community through a wide indexing policy adopted by this online international journal. Hence, they can freely be accessed and utilized by everyone for the development of science and technology.Being a part of an eco-friendly community, favors and promotes e-publication of papers to truly present itself as an online journal. </sup></p>https://ijcds.org/index.php/home/article/view/49SPEED CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTOR USING AN ARTIFICIAL INTELLIGENCE CONTROLLER 2026-02-19T16:28:47+00:00Mrs. Shivani Mishra , Dr. Rahul Mishra & Dr. Pratlbha Shukla Tripathia@gmail.com<p>Permanent Magnet Synchronous Motors (PMSMs) are widely adopted in high-performance industrial<br>drives, electric vehicles, robotics, and renewable energy systems due to their high efficiency, compact<br>size, and superior torque density. However, achieving precise and robust speed control of PMSMs<br>remains a challenging task because of nonlinear dynamics, parameter variations, load disturbances, and<br>uncertainties in operating conditions. Conventional proportional–integral (PI) controllers, although simple<br>and widely used, exhibit limited performance under such nonlinear and time-varying conditions. In recent<br>years, Artificial Intelligence (AI)–based control techniques have emerged as powerful alternatives capable<br>of learning system behavior, adapting to uncertainties, and improving dynamic performance. This paper<br>presents a comprehensive study on speed control of a PMSM using an AI controller, with emphasis on<br>intelligent techniques such as Artificial Neural Networks (ANN), Fuzzy Logic Control (FLC), and hybrid<br>neuro-fuzzy approaches. The mathematical modeling of PMSM in the d–q reference frame is discussed,<br>followed by the design methodology of the AI-based speed controller integrated with field-oriented<br>control. Performance evaluation is carried out in terms of rise time, settling time, steady-state error,<br>torque ripple, and robustness against load disturbances. Comparative analysis with conventional PI<br>control demonstrates the superiority of AI controllers in achieving faster dynamic response, reduced<br>overshoot, and improved stability. The results highlight the suitability of AI-based speed control<br>strategies for next-generation high-performance PMSM drive applications.</p>2026-02-16T00:00:00+00:00Copyright (c) 2026