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