Neural-based iterative learning control

This chapter presents a neural-based iterative learning control method for unknown nonlinear systems. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary learning manipulation and a storage of a specific temporary action. The leaxning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memorybased feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored informations. This scheme is applied to a two link robot manipulator through simulations.