In the early 1970s, fuzzy systems and fuzzy control theories added a new dimension to control systems engineering. From its beginnings as mostly heuristic and somewhat ad hoc, more recent and rigorous approaches to fuzzy control theory have helped make it an integral part of modern control theory and produced many exciting results. Fuzzy logic control is a methodology bridging artificial intelligence and traditional control theory. Fuzzy Logic can address complex control problems, such as robotic arm movement, chemical or manufacturing process control, antiskid braking systems, or automobile transmission control with more precision and accuracy, in many cases, than traditional control techniques. Fuzzy control is a methodology for expressing operational laws of a system in linguistic terms instead of mathematical equations Fuzzy logic imitates the logic of human thought, which is much less rigid than the calculations computers generally perform. The Fuzzy Logic methodology comprises three phases. 1) The fussification phase is a transformation of input variables to linguistic ones. 2) The fuzzy inference maps input linguistic variables onto output linguistic variables on the base of a system of fuzzy rules of the type “IF A THEN B”. 3) The defuzzification phase converts the weighted values of output linguistic variables obtained as a result of fuzzy inference to output variables.
