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arXiv · 1705.07826

Iterative Machine Learning for Output Tracking

Santosh Devasia
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Abstract

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.

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Santosh Devasia. 2017-05-22. Iterative Machine Learning for Output Tracking. https://doi.org/10.1109/tcst.2017.2772807

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