On-line self-organizing neuro-fuzzy control for autonomous underwater vehicles

Controlling Autonomous Underwater Vehicles (AUVs) in an uncertain and unstructured environment presents many challenging control problems. Model-based control strategies have been used with limited success. This paper presents an on-line self-organizing neuro-fuzzy control that serves as a better alternative control scheme in controlling AUVs. The proposed self-organizing neuro-fuzzy controller is a six-layer feedforward neural network that 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.