Block Hierarchical Fuzzy-Neural Networks and Their Application to a Mobile Robot Control

Abstract

If some fuzzy sets in a fuzzy-neural network are assigned to each scalar input data, then the number of intermediate unit functions grows exponentially as the number of input variables to the fuzzy reasoning increases. Therefore, it is very important for multi-input/multi-out-put systems to effectively construct a small-scale fuzzy neural network. In this paper, four types of block hierarchical fuzzy-gaussian neural networks (FGNNs) are proposed for a control system of a mobile robot with two independent driving wheels by applying two inputs and single-output FGNN block, or single-input and singleoutput FGNN block. Such a block hierarchical FGNN consists of three layers. In other words, the first input layer consists of two FGNN blocks that independently generate torques for controlling the velocity and azimuth of the mobile robot. The second hidden layer determines their distributions to the final layer by using fixed connection weights. The final output layer also consists of two FGNN bl ks that automatically determine the out put scalers for the actual left- and right-wheel driving torques. The effectiveness of the proposed method is illustrated through some simulations of a circular path tracking control.