Neural network based predictive control systems for underwater robotic vehicles

Abstract

Oceanographic exploration is one of the fast emerging applications of robotics, and the design of controllers for Underwater Robotic Vehicles (URVs) is as challenging as for land based ones. The difficulties in modelling an URV and its hazardous environment restrict the use of conventional controllers. This paper presents an approach for control and system identification of a prototype URV, as an example of a system containing severe non-linearities, using neural networks (NNs). NNs models are developed and then incorporated into a predictive control strategy which are evaluated on-line. Results are shown for both the modelling and control of the system including hybrid control strategies which combine neural predictive with conventional three term controllers. >