This article introduces a general formulation of model based iterative learning control (ILC). The formulation is valid for both linear and nonlinear systems. It is a two step approach, such that after each repetition of the task two (non)linear least squares problems have to be solved. In the first step an optimal model correction is calculated. This is a nonparametric correction to the model in order to describe the measured output signal more accurately. This model correction is used in the second step, which is a model inversion problem. Conventional linear ILC is shown to be a particular case of this general formulation.

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