Performance Enhancement of DC-Motor Based on Multi Different Control Techniques

Document Type : Research Article

Authors

1 Department of Electrical Engineering, Faculty of Engineering, October 6 University of Egypt

2 Faculty of Engineering, O6U

3 Faculty 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.

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