Fast on-line neuro-fuzzy controller for autonomous underwater vehicles

This paper describes a fast on-line neuro-fuzzy controller for underwater robots of which the dynamics are highly nonlinear, coupled, and time-varying. The neuro-fuzzy controller is based on the Fuzzy Membership Function Neural Network (FMFNN) with varying learning rate. Even though FMFNN has advantages of fuzzy logic and neural networks, it is not so easy to decide initial rules for fuzzy inference part and learning rates for neural network part. These two factors affect controller performance so much. There are many research results about how to decide or modify fuzzy rules. However learning rate was not an issue in neuro-fuzzy controller. Thus, in this paper, varying learning rate is applied to FMFNN by considering system error. To show the validity of varying learning rate method, simulation results of FMFNN with varying learning rate are presented with underwater robot control example.