Iterative learning neural network control for nonlinear system trajectory tracking

This paper presents a neural network controller for nonlinear system trajectory tracking, which works in an iterative learning manner. The controller is composed of many local neural networks and every point along the desired trajectory has its own one for approximating nonlinearity only nearby. This makes that every local neural network can be possessed of a simple structure and less neurons. Because the neural networks are independent from each other, the whole trajectory training can be divided into several segments training, where we train a segment repetitively and extend the trained segment step by step. Stability of the controller is ensured. D-Facto public., ISBN 2-930307-00-5, pp. 153-158 B orks 0, ES Netw r 0 A l 0 ug ra 2 NN Neu e l '2 l s i 000 icia pr Artif ( A p on B 8 ro m e 2 ce iu l edi pos g 6 ngs ym i 2 S u E an m , urope )