Induction Machine PWM Control Using PI and ANFIS Controllers

The control of an induction machine represents a sophisticated process requiring careful consideration of multiple factors. Pulse Width Modulation (PWM) serves as one control methodology, employing pulse signals to regulate power delivery to the machine. However, standalone PWM implementation often proves inadequate for achieving optimal performance. To enhance induction machine control, a Proportional-Integral (PI) controller can be integrated. The PI controller algorithm continuously modulates the PWM duty cycle by calculating error compensation through proportional and integral terms, maintaining stable output voltage despite system disturbances. Implementation typically involves tuning Kp and Ki parameters using Ziegler-Nichols or similar methods.

An alternative approach for performance enhancement utilizes Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This hybrid intelligent system combines neural network learning capabilities with fuzzy logic inference mechanisms, creating an adaptive controller that learns from input-output data patterns. The ANFIS architecture employs a five-layer network structure where Layer 1 generates membership functions, Layer 2 computes firing strengths, Layer 3 normalizes these strengths, Layer 4 defines output functions, and Layer 5 produces final outputs. Through MATLAB's anfis function or Python scikit-fuzzy implementation, the system can dynamically adjust control parameters, improving accuracy and robustness against operational condition variations. By synergistically combining PWM, PI control, and ANFIS methodologies, engineers can develop comprehensive control systems achieving superior performance in induction machine applications.