# Iterative Machine Learning for Output Tracking

## TL;DR

This paper introduces an iterative machine learning method using kernel-based models and augmented inputs to improve output tracking, with simultaneous model and input updates demonstrated through simulation.

## Contribution

It proposes a novel kernel-based iterative learning approach that updates both the model and input concurrently for enhanced output tracking.

## Key findings

- Kernel-based iterative updates improve tracking accuracy
- Augmented inputs with persistency of excitation enhance learning
- Simulation confirms effectiveness of the proposed method

## 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.