Fuzzy logic
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Fuzzy logic is a mathematical framework that extends classical binary logic by allowing variables to take on degrees of truth between 0 and 1, rather than being strictly true or false. Introduced by Lotfi Zadeh in 1965 through fuzzy set theory, it enables systems to reason with vague, imprecise, or uncertain information in a way that mirrors human judgment. In robotics and AI, fuzzy logic is widely used to design controllers for tasks such as mobile robot navigation, manipulator control, autonomous vehicle guidance, and exoskeleton operation, where system dynamics are complex, nonlinear, or incompletely known. Fuzzy rules—expressed as intuitive "if-then" statements—allow engineers to encode expert knowledge directly into a controller without requiring precise mathematical models. It is frequently combined with neural networks (neuro-fuzzy systems), genetic algorithms, and adaptive control schemes to improve learning and robustness. Fuzzy logic matters because it provides a practical, interpretable approach to handling real-world uncertainty, making controllers more reliable and adaptable across dynamic, unstructured environments common in modern robotic applications.
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