Fuzzy logic controllers are rule-based systems. It can be considered as expert control system as well. For fuzzy logic control, rules represent a control decision mechanicm to adjust the effects of certain causes coming from the outside source or from other systems. The aim of fuzzy logic controller is usually as a replacement for skilled human operator. In order to efficiently control certain system using fuzzy logic controller, human must first studies and performs quantitative analysis to the systems to be controlled. This is ensure the best membership functions and rulescan be formed up so that the fuzzy logic controller can produce an excellent control result. Fuzzy logic controller builds up from fuzzifier, fuzzy inference engine, defuzzifier dand rule-base. For this ffuzzy logic controller, part of the fuzzification process is done by human. Humans have to convert the result of the quantitative analysis to membership functions and rules which are the form of knowledge that can be understand and recognized by the fuzzy logic controller. These rules and membeship functions are the basic components that build up the rule-base. The other part of fuzzification process is done by fuzzifier. This part of fuzzification process is actually the matching process between memermembership functions and the crisp input that coming from outside source. The matched membership functions are sent to fuzzy inference engine for the fuzzy inference process. The fuzzy inference engine will go through the IF-THEN rules whose antecedents and consequents are themselves membership functions. The fuzzy inference engine calculates the degree of these membership functions using the crisp input. The degrees of consequent membership functions from different rules are numerically combined and then send to defuzzifiermfor deffuzzification process. The defuzzzifier will calculate the cnetroid for consequent membership functions with their degree and yield a single real number which is the output of the fuzzy logic controller.