Hardware Implementation of Fuzzy Controllers

Fuzzy logic control is a methodology bridging artificial intelligence and traditional control theory. This methodology is usually applied in the only cases when accuracy is not of high necessity or importance. On the other hand, as it is stated in (TI SPRA028, Jan.1993), “Fuzzy Logic can address complex control problems, such as robotic arm movement, chemical or manufacturing process control, antiskids braking systems or automobile transmission control with more precision and accuracy, in many cases, than traditional control techniques ... . Fuzzy Logic is a methodology for expressing operational laws of a system in linguistic terms instead of mathematical equations.” Wide spread of the fuzzy control and high effectiveness of its applications in a great extend is determined by formalization opportunities of necessary behavior of a controller as a “fuzzy” (flexible) representation. This representation usually is formulated in the form of logical (fuzzy) rules under linguistic variables of a type “If A then B”. The Fuzzy Logic methodology (Yager & Zadeh, 1992; Klir & Yuan, 1996) comprises three phases: 1. The fuzzification is a transformation of analog (continuous) input variables to linguistic ones, e.g., transformation of temperature into the terms cool, warm, hot or transformation of speed into the terms negative big (NB), negative small (NS), zero (Z)”, positive small (PS), positive big (PB). Such transformation is realized by introduction of so-called membership functions, which define both a range of value and a degree of membership. For linguistic variables it is important not only which membership function a variable belongs to, but also a relative degree (weight) to which it is a member. A variable can have a weighted membership in several membership functions at the same time. 2. The fuzzy inference maps input linguistic variables onto output linguistic variables on the base a system of fuzzy rules of the type “IF A THEN B” For instance: “IF the temperature is worm THEN the speed is Positive Small (PS)” or “IF the speed is Negative Big (NB) THEN force is ZERO”. Since input linguistic variables are weighted, the output linguistic variables can be obtained weighted as well. Traditional fuzzy logic approach comprises Mamdanitype and Sugeno-type inference methods. The Mamdani-type