Deploying Fuzzy Logic for Self-Tuning Regulator Design for Motion Control in Modern Electrical Machines

Year : 2025 | Volume : 03 | Issue : 02 | Page : 11 21
    By

    K.V.V. Subba Rao,

  • Anantham Srujana Jyothi,

  • Manas Kumar Yogi,

  1. Assistant Professor, CSE Department , Pragati Engineering College (A), Surampalem, Andhra Pradesh, India
  2. Assistant Professor, CSE-AI & MIL Department Pragati Engineering College (A), Surampalem, Andhra Pradesh, India
  3. Assistant Professor, CSE Department , Pragati Engineering College (A), Surampalem, Andhra Pradesh,

Abstract

Modern electrical machines require sophisticated motion control systems capable of adapting to varying operating conditions, load disturbances, and parameter uncertainties. Traditional self-tuning regulators (STR) based on classical control theory often struggle with nonlinearities, time-varying dynamics, and complex operational environments characteristic of contemporary electric drives. This article presents a comprehensive framework for deploying fuzzy logic in self-tuning regulator design to address these challenges in motion control applications. Fuzzy logic controllers leverage linguistic rules and approximate reasoning to handle uncertainty and imprecision without requiring precise mathematical models. For next-generation motion control systems, the fuzzy-enhanced STR architecture provides a number of strategic benefits beyond the fundamental gains in steady-state accuracy and dynamic response. Its capacity to handle nonlinear machine characteristics—such as magnetic saturation, inverter dead-time effects, temperature- dependent resistance changes, and mechanical friction—without necessitating explicit analytical modeling of these behaviors is one important advantage. Rather, the fuzzy inference process uses expert-defined linguistic rules to analyze variances in system performance and modify controller gains. This speeds up development and increases adaptability, particularly in applications where it’s difficult to get precise system characteristics. The proposed fuzzy-based STR architecture incorporates online parameter estimation, adaptive gain tuning, and intelligent decision-making mechanisms. Applications to permanent magnet synchronous motors (PMSM), induction motors, and switched reluctance motors demonstrate significant improvements in tracking accuracy, disturbance rejection, and robustness compared to conventional PID and fixed-parameter controllers. Experimental validation shows 35-45% reduction in steady-state error and 20-30% improvement in settling time under variable load conditions. This work provides practical design guidelines, implementation strategies, and performance evaluation methodologies for integrating fuzzy intelligence into motion control systems for industrial automation, electric vehicles, robotics, and renewable energy applications.

Keywords: self-tuning regulators (STR), permanent magnet synchronous motors (PMSM), electric vehicles, robotics, proportional-integral-derivative (PID).

[This article belongs to International Journal of Electrical Machine Analysis and Design ]

How to cite this article:
K.V.V. Subba Rao, Anantham Srujana Jyothi, Manas Kumar Yogi. Deploying Fuzzy Logic for Self-Tuning Regulator Design for Motion Control in Modern Electrical Machines. International Journal of Electrical Machine Analysis and Design. 2025; 03(02):11-21.
How to cite this URL:
K.V.V. Subba Rao, Anantham Srujana Jyothi, Manas Kumar Yogi. Deploying Fuzzy Logic for Self-Tuning Regulator Design for Motion Control in Modern Electrical Machines. International Journal of Electrical Machine Analysis and Design. 2025; 03(02):11-21. Available from: https://journals.stmjournals.com/ijemad/article=2025/view=235600


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Regular Issue Subscription Original Research
Volume 03
Issue 02
Received 17/11/2025
Accepted 19/11/2025
Published 31/12/2025
Publication Time 44 Days
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