Neuro-fuzzy control of a mobile robot

Fuzzy systems are able to treat uncertain and imprecise information; they make use of knowledge in the form of linguistic rules. Their drawbacks are caused mainly by the difficulty of defining accurate membership functions and lack of a systematic procedure for the transformation of expert knowledge into the rule base. Neural networks have the ability to learn but with some neural networks, knowledge representation and extraction are difficult. First, we consider a rule-based fuzzy controller and a learning procedure based on the stochastic approximation method. The radial basis function neural network is then considered and it is shown that a modified form of this network is identical with the fuzzy controller, which may thus be considered as a neuro-fuzzy controller. Numerical examples are presented to demonstrate the validity of the approach and it is shown that such an adaptive neuro-fuzzy system is successful in the control of a mobile robot. (C) 1999 Published by Elsevier Science B.V. All rights reserved.