A neural-network-based controller for a single-link flexible manipulator: Comparison of FFNN and DRNN controllers

This paper employs two types of neural networks to control a single-link flexible arm. To train each network, we utilize a gradient-based approach with adaptive learning rate. We first apply the diagonal recurrent neural network (DRNN) to a single-link flexible arm, which is a challenging control problem, in order to investigate the ability of this type of recurrent neural network. We then apply a feed-forward neural network (FFNN) to this...