A Fuzzy Self-Tuning PID Controller

To overcome the limitations of traditional PID controllers in parameter tuning, fuzzy control is integrated with PID to construct a fuzzy self-tuning PID controller. This controller utilizes fuzzy logic to handle system nonlinearities and uncertainties while leveraging the strengths of PID control for precise regulation. In implementation, fuzzy inference systems (e.g., Mamdani or Sugeno types) dynamically adjust proportional, integral, and derivative gains (Kp, Ki, Kd) based on real-time error (e) and error derivative (de/dt). The fuzzy rule base, typically designed with linguistic variables like "positive large" or "negative small," enables the system to maintain stability and robustness under varying operating conditions. By incorporating fuzzy control, the challenges of manual PID tuning are mitigated, resulting in enhanced control system performance and adaptability to environmental changes. Code implementation often involves defining membership functions, rule evaluation, and defuzzification methods (e.g., centroid or bisector) to compute optimal PID parameters.