Implementation of PID Controller Parameter Tuning Using Fuzzy Control Theory

We have programmed the implementation of PID controller parameter tuning using fuzzy control theory in the MATLAB environment, and conducted step response simulation experiments on known system models. The implementation utilizes MATLAB's Fuzzy Logic Toolbox to design membership functions and rule bases for intelligent parameter adjustment.

To achieve PID parameter tuning through fuzzy control theory, we developed MATLAB scripts that implement fuzzy inference systems. These systems dynamically adjust proportional, integral, and derivative gains based on error and error rate inputs using Mamdani-type fuzzy inference. The implementation enhances system performance by providing adaptive control capabilities and improves system stability through real-time parameter optimization.

Furthermore, we performed step response simulation experiments on predefined system models using MATLAB's Control System Toolbox. These experiments allowed us to evaluate system response characteristics, including rise time, settling time, and overshoot, and understand system behavior under various input conditions. The simulation framework includes comparative analysis between conventional PID and fuzzy-tuned PID controllers.

In summary, our MATLAB implementation successfully applies fuzzy control theory for PID parameter tuning and conducts comprehensive step response simulations on known models. This approach significantly improves system performance metrics and enhances stability through intelligent, adaptive control strategies implemented via fuzzy logic algorithms.