Acquisition of Fuzzy Control Rules Using Soft Computing Technique

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Fuzzy control rules used in fuzzy controls are usually constructed through trial and error by human operators. But it becomes difficult to construct the fuzzy rules by humans when the controlled plants are complicated.This project aims to acquire fuzzy control rules automatically by using soft computing techniques in tuning the antecedent and consequent parts fuzzy rules.New fuzzy reasoning methods for fuzzy controls are proposed such as "fuzzy singleton-type reasoning method", "fuzzy functional reasoning method" and "weighted functional reasoning method", and their relationships are investigated.We give a method of tuning the center and width of the antecedent part as well as the position and weight of the consequent part of fuzzy control rules of fuzzy singleton-type reasoning method by using the deepest decendent method used in the neuro technique and fuzzy clustering method. It is shown from the computer simulation of several functions that this method can generate smaller number of fuzzy control rules compared with the case by the simplified reasoning method which is used usually and widely in fuzzy controls.We propose a distance-type fuzzy reasoning method based on the calculated distance between two fuzzy sets, and discuss robust associative memory algorithm, dynamic reasoning algorithm and unterpolation algorithm in the characteristic space using distant-type fuzzy reasoning method.We consider evolvable hardware using field programmable gate array to run autonomous mobile robot Khepera for collision avoidance. As a new type of chromosome coding in genetic algorithm, tree structure-type chromosome coding is proposed. Moreover, we construct a robot with 4 legs and demonstrate the balance and position controls under fuzzy controls.