A Modified Hybrid Fuzzy Controller for Real-Time Mobile Robot Navigation

Abstract

The Fuzzy hybridization technique for intelligent systems have become of research interests in a variety of research areas over past the decades. There are limitations faced by all popular fuzzy systems architectures when they are applied to applications with a large number of inputs (Multi-sensors) systems. In this paper, proposes a modified hybrid fuzzy controller for multi-sensors (Large number of inputs) real-time mobile robot navigation. A modified hybrid fuzzy controller is constructed by the automatic generation of membership functions (MFs) and formed a minimal numbers of rules using hybrid fuzzy clustering algorithm (Combination of Fuzzy C-means and Subtractive clustering algorithm) and the modified apriori algorithm, respectively. The generated hybrid fuzzy controller is then adjusted by the least square method and the gradient descent algorithm towards better performance with a minimal set of rules. The performance is observed in an application for real-time mobile robot navigation has been found to be very impressive and competitive.