  This article develops iterative machine learning (IML) for output tracking.
The input-output data generated during iterations to develop the model used in
the iterative update. The main contribution of this article to propose the use
of kernel-based machine learning to iteratively update both the model and the
model-inversion-based input simultaneously. Additionally, augmented inputs with
persistency of excitation are proposed to promote learning of the model during
the iteration process. The proposed approach is illustrated with a simulation
example.
