Fuzzy Logic in Artificial Intelligence: An Engineering-Oriented Study with Intelligent Temperature Control and Mobile Robot Applications

Artificial intelligence is increasingly integrated into engineering systems that must make decisions under uncertainty, noisy measurements, and nonlinear operating conditions. Fuzzy logic provides a practical framework for representing gradual concepts and converting linguistic engineering knowledge into quantitative decisions. This paper presents an engineering-oriented investigation of fuzzy logic as a component of artificial intelligence. Two numerical case studies are developed using Mamdani inference and centroid defuzzification. The first considers intelligent temperature control, where temperature is transformed into a continuous fan-speed command. The second develops a fuzzy obstacle-avoidance controller for a mobile robot using obstacle distance and robot velocity as inputs and steering angle as the output. The simulations show how overlapping membership functions and linguistic rules generate gradual engineering actions. For the temperature controller, a temperature of ۳۱ °C produces a fan command of ۷۷.۱۸%. For the robot controller, an obstacle distance of ۰.۶۵ m and velocity of ۱.۲۰ m/s produce a steering command of −۲۷.۲۴°. The paper further discusses fuzzy decision support, fault diagnosis, ANFIS, explainable AI, and future directions including adaptive fuzzy systems, fuzzy reinforcement learning, and edge intelligence.

کلیدواژه ها:

Fuzzy Logic ، Artificial Intelligence ، Fuzzy Control ، Robotics ، Explainable AI.