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 language | English |
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Pages (from-to) | 86-91 |
Number of pages | 6 |
Journal | IFAC-PapersOnLine |
Volume | 58 |
Issue number | 18 |
DOIs | |
Publication status | Published - 1 Aug 2024 |
Event | 8th IFAC Conference on Nonlinear Model Predictive Control, NMPC 2024 - Kyoto, Japan Duration: 21 Aug 2024 → 24 Aug 2024 |
Bibliographical note
Publisher Copyright:Copyright © 2024 The Authors.
Funding
Funders | Funder number |
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European Commission | |
H2020 European Research Council | 101055384 |
Keywords
- data-driven control
- frequency-response-function measurements
- Model predictive control