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arXiv · 2502.21094

System Identification Beyond the Nyquist Frequency: A Kernel-Regularized Approach

Max van Haren·Roy S. Smith·Tom Oomen
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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.

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Max van Haren, Roy S. Smith, Tom Oomen. 2025-06-17. System Identification Beyond the Nyquist Frequency: A Kernel-Regularized Approach. https://doi.org/10.1016/j.conengprac.2025.106425

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