Fuzzy Control Based on Cluster Analysis and Dynamic Programming

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Abstract:

This paper focuses on fuzzy control of a class of nonlinear systems, which are characterized by model uncertainty and inequality model constraints. The associated Intelligent Information System (IIS) is designed to store the results from possible training made by an expert and distributed via network. The paper considers cluster analysis for such a system, based on Bezdek’s fuzzy cluster method (FCM). The proposed method is used to classify the input data and to extract the rules.
An example of fuzzy control for autonomous mobile system in 3D space is explored and the results from the decision using the method of dynamic programming in fuzzy environment are shown. The synthesized algorithm guides an autonomous vehicle in 3D space which pursues an object and evades an obstacle. The fuzzy control is based on determination of a maximizing decision by using dynamic programming. The maximizing decision is defined as a point in the space of alternatives at which the membership function of a fuzzy decision attains its maximum value. The purpose of the presented algorithm is to demonstrate a fuzzy method for determination of the trajectory of the dynamic object.