Titlebook: Iterative Learning Control; Analysis, Design, In Zeungnam Bien,Jian-Xin Xu Book 1998 Springer Science+Business Media New York 1998 Nonlinea

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The Frontiers of Iterative Learning Controlve into all control and application fields. This chapter mainly discusses ILC frontiers from the task and system levels. We will also briefly address other important issues associated with ILC such as implementation, intelligence, design and applications, which will be further detailed in other chapters of this book.

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On the Iterative Learning Control of Sampled-Data Systemsonvergence and robustness. Under a sufficient condition on the learning operator, the uniform boundedness between the plant output and the desired output can be shown at each sampling instant if the sampling period is small enough.

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Neural-Based Iterative Learning Controlol 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.

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Robust ILC with Current Feedback for Uncertain Linear Systemsedback controller and learning controllers can be designed at one time and a weighting function is introduced to increase the learning performance. Finally, through a computational experiment, we confirm the feasibility of the proposed method.

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Model Reference Learning Control with a Wavelet Networks approximated by a wavelet network. A detailed design of the MRLC with a wavelet network is presented and boundedness of the estimation error is proved through theoretical analysis. A simple numerical simulation is also presented.

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