Self-adaptive neuro-fuzzy control with fuzzy basis function network for autonomous underwater vehicles

Abstract

Presents an online self-adaptive neuro-fuzzy control that serves as a better alternative control scheme in controlling autonomous underwater vehicles (AUVs) in an uncertain and unstructured environment. The proposed self-adaptive neuro-fuzzy controller is a five-layer feedforward neural network that implements fuzzy basis function (FBF) expansions and is capable of self-constructing and self-restructuring its internal node connectivity and learning the parameters of each node based on incoming training data. Computer simulations have been conducted to validate the performance of the proposed neuro-fuzzy controller and an experimental verification has been scheduled to verify it on ODIN, an autonomous underwater vehicle developed at the University of Hawaii.