Performance Enhancement of DC-Motor Based on Multi Different Control Techniques | ||
| International Journal of Engineering and Applied Sciences-October 6 University | ||
| Article 4, Volume 2, Issue 2, July 2025, Pages 38-44 PDF (701.14 K) | ||
| Document Type: Research Article | ||
| DOI: 10.21608/ijeasou.2025.387627.1052 | ||
| Authors | ||
| AMGAD Salem* 1; Mohamed Abdel-Ghany2; ٍShorouk Ossama Ibrahim3 | ||
| 1Department of Electrical Engineering, Faculty of Engineering, October 6 University of Egypt | ||
| 2Faculty of Engineering, O6U | ||
| 3Faculty of engineering October 6 university | ||
| Abstract | ||
| : DC servo motors are critical in automation, robotics, and precision control systems due to their rapid response and high torque capabilities. However, traditional Proportional-Integral-Derivative (PID) controllers suffer from inefficiencies in dynamic environments, including manual tuning challenges and poor adaptability to nonlinearities. This study systematically evaluates advanced control strategies—Genetic Algorithm (GA)-optimized PID, Self-Tuning PID, Fuzzy Logic Control (FLC), Fractional-Order PID (FOPID), and Relative Rate Observer (RRO)-based Self-Tuning Fuzzy PID (FPID)—to address these limitations. Through MATLAB/Simulink simulations, each method is assessed using performance metrics such as settling time, overshoot, and robustness under parametric variations. Results demonstrate that GA-optimized PID reduces overshoot by 40%, while the RRO-based Self-Tuning FPID achieves the fastest settling time (0.6 seconds) and near-zero steady-state error. The Self-Tuning FOPID controller emerges as the most robust, combining fractional calculus with real-time adaptation. This study underscores the synergy between computational intelligence and control theory, proposing future integration of deep learning for enhanced real-time optimization. | ||
| Keywords | ||
| DC servo motor; PID control; Genetic Algorithm; Fuzzy logic; Fractional-order control | ||
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