Multiple Model Control Using Neural Networks for a Remotely Operated Vehicle

This paper considers the tracking control problem of an underwater vehicle subjected to different load configurations, which from time to time introduce considerable variations of its mass and inertial parameters. The control of this kind of mode-switch process can not be adequately faced with traditional adaptive control techniques because of the too long time needed for adaptation. To cope with this problem, a switching control scheme is proposed and multiple neural network-based plant models are defined to describe the different possible operative conditions of the vehicle. The performance of the switched controller is evaluated by numerical simulations