Yadi Mulyadi, Erik Haritman, Muhammad Azhar Robbani, Muhammad Adli Rizqulloh
This paper presents a simulation approach for induction motor speed control utilizing a Fuzzy Logic Algorithm, specifically comparing the performance of a conventional Proportional (P) controller with a Proportional-Fuzzy controller. Three-phase squirrel cage induction motors are widely used in industrial applications, but their inherent nonlinearity, coupling effects, and varying parameters pose significant challenges for precise control using conventional methods. While traditional PI and PID controllers are prevalent, they often struggle with performance sensitivity to parameter variations, high load disruptions, and nonlinearities, potentially degrading dynamic performance. To address these limitations, Fuzzy Logic Controllers are increasingly employed as adaptive speed controllers, offering advantages such as independence from precise mathematical models and robustness against uncertainties and disturbances. This study evaluates the efficacy of the designed Fuzzy Logic Controller using MATLAB/SIMULINK, employing a 10 HP squirrel cage induction motor model. The simulation results indicate that although the P controller exhibits superior performance in specific transient metrics (e.g., settling time, rising time, steady-state error, peak overshoot), it critically generates negative speed, indicating an undesirable reversal of rotor direction. In contrast, the P-fuzzy controller effectively prevents negative speed, demonstrating its robust capability to maintain the intended rotor direction throughout operation. This research affirms the enhanced operational reliability of P-fuzzy control strategies for induction motor speed regulation, particularly in mitigating undesirable speed reversals. © 2025 IEEE.
Renewable Energy Engineering, Universitas Pendidikan Indonesia, Bandung, Indonesia; Industrial Automation and Robotic Engineering Education, Universitas Pendidikan Indonesia, Bandung, Indonesia; Universitas Pendidikan Indonesia, Industrial Internet of Things Laboratory, Bandung, Indonesia; King Fahd University of Petroleum and Minerals, Department of Computer Engineering, Dhahran, Saudi Arabia
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