A Fuzzy PID Controller Simulink Model Implementation

This paper presents a comprehensive implementation of a fuzzy PID controller using Simulink. The model establishes a fuzzy logic-based PID controller that dynamically adjusts control outputs based on error signals between input and output measurements. We incorporate fuzzy logic principles to effectively handle nonlinear system characteristics. The implementation details include creating fuzzy inference systems using MATLAB's Fuzzy Logic Toolbox, designing membership functions for error and error rate inputs, and developing rule bases for intelligent PID parameter adaptation. The content provides step-by-step guidance on Simulink block configuration, including Fuzzy Logic Controller blocks, PID controller blocks, and system integration techniques. Practical examples demonstrate parameter optimization strategies using techniques like trial-and-error adjustment, automated tuning algorithms, and performance evaluation metrics such as ISE (Integral Square Error) and IAE (Integral Absolute Error) for achieving superior control performance.