Neuro-fuzzy control of a mobile robot

Fuzzy systems are able to treat uncertain and imprecise information; they m ake use of knowledge in the form of linguistic rules. Their drawbacks are c aused mainly by the difficulty of defining accurate membership functions an d lack of a systematic procedure for the transformation of expert knowledge into the rule base. Neural networks have the ability to learn but with som e neural networks, knowledge representation and extraction are difficult. F irst, we consider a rule-based fuzzy controller and a learning procedure ba sed on the stochastic approximation method. The radial basis function neura l network is then considered and it is shown that a modified form of this n etwork is identical with the fuzzy controller, which may thus be considered as a neuro-fuzzy controller. Numerical examples are presented to demonstra te 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 Publ ished by Elsevier Science B.V. All rights reserved.