Fuzzy logic control: Design of a ‘Mini’ fuzzy associative matrix (FAM) table algorithm in motor speed control

In fuzzy system, fuzzy processing or inference engine is the stage where the controller analyzes and evaluates the fuzzy inputs using IF-THEN conditional statements. The IF-THEN structure is basically applicable to many types of problems and until now, this concept is still considered to be the most popular algorithms implemented. Fuzzy rule evaluation can be performed using fuzzy associative matrix (FAM) table or rule matrix. FAM table is very useful in representing the rule editor into matrix form showing all possible outputs for all possible inputs. It is often employed in system when there are two (2) fuzzy inputs with their terms logically related using AND. FAM table is heavily dependent on the number of fuzzy inputs and their corresponding membership functions. As the number of fuzzy inputs increases, the amount of computational rules also increases. With that, `mini' fuzzy associative matrix (FAM) table algorithm was introduced in the study to reduce the number of computational rules from the original FAM table of a 2-input, single-output fuzzy control system. In this study, the concept of motor speed control was used. A total of 36 rules were generated from the combination of two (2) inputs with six (6) membership functions each. Using this algorithm, the number of computational rules were reduced to 75%. Making the process very useful in simplifying fuzzy programs.