Fuzzy System Tuned PI Controller Implementation in BLDC Motor Control

The implementation of PI controllers in BLDC motor speed regulation represents a widely adopted control strategy. To enhance controller precision, fuzzy logic systems can be integrated for dynamic parameter tuning, enabling more accurate motor speed control with improved operational efficiency. In fuzzy system implementation, input signals such as speed error and error derivative are converted into linguistic variables using membership functions, which form the basis for fuzzy rule generation. The rule base typically consists of IF-THEN statements that define controller behavior under various operating conditions. Code implementation involves defining fuzzy sets for inputs (e.g., Negative Big, Zero, Positive Big) and outputs (Kp and Ki adjustments), followed by rule evaluation using Mamdani or Sugeno inference methods. Defuzzification techniques like centroid calculation convert fuzzy outputs into crisp PI parameter values. Through fuzzy-based PI tuning, motor speed regulation achieves enhanced accuracy with smoother operation characteristics, while significantly improving system response time and reducing overshoot/oscillation tendencies. The combined fuzzy-PI controller architecture demonstrates superior performance in BLDC motor applications, particularly in handling non-linearities and varying load conditions.