Neural network application to capture control in aqua-robot manipulator

This paper deals with implementation of a neural network (NN) algorithm to an aqua-robot manipulator control to capture a floating object in water, and an evaluation of the NN control performance in comparison with that of the conventional PD feedback control. The nonlinear dynamics of a floating object in water, caused by approaching motion of the aqua-robot manipulator to the object, is learned in a three-layered neural network and the neural network is implemented in the control system of the aqua-robot manipulator in parallel to a conventional PD feedback controller. The performance of the neural network capture control is experimentally evaluated and found essentially high in terms of the capture time needed and capture-success ratio.