Induction Machine PWM Control using PI and ANFIS: Implementation and Comparison

Induction machines are widely used in industrial applications due to their robustness and reliability. Efficient control of speed and torque requires advanced techniques, with Proportional-Integral (PI) control and Adaptive Neuro-Fuzzy Inference System (ANFIS) being two prominent methods typically combined with Pulse Width Modulation (PWM) for precise motor drive regulation. PI Controller for Induction Machine Control A PI controller employs traditional feedback mechanisms to adjust motor input voltage for maintaining desired speed or torque. The proportional component responds to instantaneous error (difference between setpoint and actual output), while the integral component eliminates steady-state errors through cumulative correction. When implementing PI control with PWM, the controller modulates inverter switching frequency using duty cycle calculations like: duty_cycle = Kp*error + Ki*∫error dt. This ensures smooth motor operation, though PI controllers may exhibit limitations handling nonlinearities and dynamic load changes due to fixed gain parameters. ANFIS-Based Control for Enhanced Performance ANFIS integrates neural network learning capabilities with fuzzy logic inference, creating an adaptive control system that self-optimizes based on operational data. The implementation typically involves a five-layer architecture: 1) input fuzzification, 2) rule application, 3) normalization, 4) consequent parameter calculation, and 5) output aggregation. For PWM-controlled induction machines, ANFIS dynamically adjusts control parameters using Sugeno-type fuzzy inference, outperforming fixed-gain PI controllers in managing nonlinearities, load disturbances, and parameter variations through online learning algorithms. Comparison and Hybrid Approaches PI control offers simplicity and reliability with straightforward code implementation (e.g., using MATLAB's pidtune function for gain optimization), while ANFIS provides superior adaptability in complex scenarios through neuro-fuzzy toolbox functions like anfis for training and gensurf for surface visualization. A hybrid approach implementing ANFIS-based PI gain scheduling can leverage both methods' strengths - using ANFIS to dynamically adjust Kp and Ki parameters based on real-time performance metrics. This combination ensures precise speed regulation with reduced torque ripple, often implemented through embedded C code or Simulink blocks for industrial drive applications. The integrated system demonstrates improved energy efficiency and robustness, making it suitable for high-performance industrial drives requiring adaptive control capabilities under varying operational conditions.