System Identification Beyond the Nyquist Frequency: A Kernel-Regularized Approach
Abstract
Models that contain intersample behavior are important for control design of systems with slow-rate outputs. The aim of this paper is to develop a system identification technique for fast-rate models of systems where only slow-rate output measurements are available, e.g., vision-in-the-loop systems. In this paper, the intersample response is estimated by identifying fast-rate models through least-squares criteria, and the limitations of these models are determined. In addition, a method is developed that surpasses these limitations and is capable of estimating unique fast-rate models of arbitrary order by regularizing the least-squares estimate. The developed method utilizes fast-rate inputs and slow-rate outputs and identifies fast-rate models accurately in a single identification experiment. Finally, both simulation and experimental validation on a prototype wafer stage demonstrate the effectiveness of the framework.
Cite
@article{arxiv.2502.21094,
title = {System Identification Beyond the Nyquist Frequency: A Kernel-Regularized Approach},
author = {Max van Haren and Roy S. Smith and Tom Oomen},
journal= {arXiv preprint arXiv:2502.21094},
year = {2025}
}