Use of neural networks in iterative learning control systems

This paper presents an approach to the use of neural networks to improve iterative learning control performance. The neural networks are used to estimate learning gain of an iterative learning law and to store the learned control input prooles for diierent reference trajectories. A neural network of piecewise linear approximation is presented to eeectively identify the system dynamics, and the approximation property and persistently exciting condition are discussed. In addition, training of a feedfor-ward neuro controller is presented to accumulate control information learned by an iterative update law for various reference trajectories. Then, an iterative learning law with feedforward neuro controller is suggested and its convergence property is stated with the convergence condition. The eeectiveness of the present methods has been demonstrated through simulations by applying them to a two link robot manipulator.