DC Motor Control Using Fuzzy Logic Controllers with Algorithm Implementation

The implementation of fuzzy logic control for DC motors involves designing a rule-based system that processes input variables (such as speed error and error rate) to generate precise control signals. The core algorithm typically incorporates three stages: fuzzification of inputs using membership functions (e.g., triangular or trapezoidal), inference engine processing through IF-THEN rules (e.g., "IF error is positive_large AND error_rate is negative_small THEN voltage_change is positive_medium"), and defuzzification using methods like centroid calculation to produce crisp output values. Key functions include defining linguistic variables, constructing rule matrices, and implementing real-time feedback loops. This approach enables adaptive motor behavior adjustment under varying load conditions, outperforming traditional PID controllers in handling non-linear systems. The fuzzy controller's parameters can be optimized through MATLAB's Fuzzy Logic Toolbox functions (fis, evalfis) or Python libraries like scikit-fuzzy, allowing customization for specific motor characteristics and operational requirements.