Induction machine PWM control using PI and ANFIS

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Induction machine PWM control using PI and ANFIS

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Induction machines are widely used in industrial applications due to their robustness and reliability. Controlling their speed and torque efficiently requires advanced control techniques. Two common approaches are Proportional-Integral (PI) control and Adaptive Neuro-Fuzzy Inference System (ANFIS), often combined with Pulse Width Modulation (PWM) for precise motor drive regulation.

### PI Controller for Induction Machine Control A PI controller is a traditional feedback-based method that adjusts the motor's input voltage to maintain desired speed or torque. The proportional term reacts to the current error (difference between desired and actual output), while the integral term eliminates steady-state errors over time. When integrated with PWM, the PI controller modulates the switching frequency of the inverter, ensuring smooth and efficient motor operation. However, PI controllers may struggle with nonlinearities and dynamic load changes.

### ANFIS-Based Control for Enhanced Performance Unlike conventional PI control, ANFIS combines neural networks and fuzzy logic, enabling adaptive and intelligent control. ANFIS learns from operational data, adjusting its parameters to optimize performance under varying conditions. When applied to PWM-controlled induction machines, ANFIS can handle nonlinearities, load disturbances, and parameter variations more effectively than fixed-gain PI controllers.

### Comparison and Hybrid Approaches While PI control offers simplicity and reliability, ANFIS provides adaptability and improved response in complex scenarios. A hybrid approach—using ANFIS for tuning PI gains dynamically—can leverage the strengths of both methods, enhancing efficiency and robustness in motor control applications.

This combination ensures precise speed regulation, reduced torque ripple, and better energy efficiency, making it suitable for high-performance industrial drives.