Iterative Learning Controller for Manipulators Based on the RBF Network

In view of the slow convergence speed of an iterative learning controller in the trajectory tracking of manipulators, a kind of new iterative learning controller based on RBF neural network is proposed from considerations of past experience in tracking various trajectories so as to select the initial control input of iterative learning controller properly. A new desired trajectory can be decomposed into many query points at first, and the RBF network is applied to construct inverse dynamics of the manipulator by fitting the nearest k data points around each query point and then predicting the initial control input. The method of control is verified by computer simulation for a planar two-link manipulator.