Kernel-based identification using Lebesgue-sampled data

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Abstract

Sampling in control applications is increasingly done non-equidistantly in time. This includes applications in motion control, networked control, resource-aware control, and event-based control. Some of these applications, like the ones where displacement is tracked using incremental encoders, are driven by signals that are only measured when their values cross fixed thresholds in the amplitude domain. This paper introduces a non-parametric estimator of the impulse response and transfer function of continuous-time systems based on such amplitude-equidistant sampling strategy, known as Lebesgue sampling. To this end, kernel methods are developed to formulate an algorithm that adequately takes into account the bounded output uncertainty between the event timestamps, which ultimately leads to more accurate models and more efficient output sampling compared to the equidistantly-sampled kernel-based approach. The efficacy of our proposed method is demonstrated through a mass–spring damper example with encoder measurements and extensive Monte Carlo simulation studies on system benchmarks.

Original languageEnglish
Article number111648
Number of pages13
JournalAutomatica
Volume164
DOIs
Publication statusPublished - Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Funding

This work is part of the research program VIDI with project number 15698, which is (partly) financed by the Netherlands Organization for Scientific Research (NWO).

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

    Keywords

    • Event-based sampling
    • Impulse response estimation
    • Kernel-based methods
    • Regularization
    • System identification

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