Frequency-Domain Data-Driven Predictive Control

Research output: Contribution to journalConference articlepeer-review

3 Downloads (Pure)

Abstract

In this paper, we propose a data-driven predictive control scheme based on measured frequency-domain data of the plant. This novel scheme complements the well-known data-driven predictive control (DeePC) approach based on time series data. To develop this new frequency-domain data-driven predictive control (FreePC) scheme, we introduce a novel version of Willems' fundamental lemma based on frequency-domain data. By exploiting frequency-domain data, we allow recent direct data-driven (predictive) control methodologies to benefit from the available expertise and techniques for non-parametric frequency-domain identification in academia and industry. We prove that, under appropriate conditions, the new FreePC scheme is equivalent to the corresponding DeePC scheme. The strengths of FreePC are demonstrated in a numerical case study.

Original languageEnglish
Pages (from-to)86-91
Number of pages6
JournalIFAC-PapersOnLine
Volume58
Issue number18
DOIs
Publication statusPublished - 1 Aug 2024
Event8th IFAC Conference on Nonlinear Model Predictive Control, NMPC 2024 - Kyoto, Japan
Duration: 21 Aug 202424 Aug 2024

Bibliographical note

Publisher Copyright:
Copyright © 2024 The Authors.

Funding

FundersFunder number
European Commission
H2020 European Research Council101055384

    Keywords

    • data-driven control
    • frequency-response-function measurements
    • Model predictive control

    Fingerprint

    Dive into the research topics of 'Frequency-Domain Data-Driven Predictive Control'. Together they form a unique fingerprint.

    Cite this