Simple Fuzzy Logic Control Demonstration

This demonstration showcases a simple fuzzy control implementation. Users can configure various input parameters and evaluate the corresponding fuzzy control responses. Fuzzy logic control is a methodology that handles uncertainty and ambiguity in input values to determine appropriate control actions. This demo employs a straightforward example to illustrate fundamental concepts and operational principles of fuzzy control systems. The implementation typically involves three key stages: fuzzification of crisp inputs using membership functions, inference engine processing with rule-based decision making, and defuzzification to produce precise output values. The code architecture may include configurable membership functions for input variables (e.g., triangular or trapezoidal shapes), a rule base containing IF-THEN statements, and defuzzification methods like centroid calculation. Key algorithmic components include: - Membership function definitions mapping numerical inputs to fuzzy sets - Rule evaluation using min-max operations for inference - Defuzzification techniques converting fuzzy outputs to crisp control signals This demonstration allows users to modify input ranges, adjust rule weights, and observe how fuzzy logic handles transitional states between discrete control boundaries, providing practical insights into robust control system design under uncertain conditions.