Abstract:Data-enabled predictive control (DeePC) has recently attracted attention as a promising approach for controlling systems directly from raw data, without requiring an explicit identification step. However, DeePC has not yet been extended to piecewise affine (PWA) systems, despite their extensive use in the (predictive) control literature and their universal approximation capabilities. To address this gap, in this work, we lay the foundations for data-enabled predictive control of PWA systems, providing: $(i)$ their behavioral characterization; $(ii)$ an extension of Willems' Fundamental Lemma to represent their behavior from raw data; $(iii)$ an analysis of the coherence of DeePC strategies using a linear predictor and shrinkage regularizers; and $(iv)$ a study of the impact of misclassification errors on structuring data for prediction. Our theoretical findings are validated by numerical results on a simple example, emphasizing the need to extend beyond a regularized version of the foundational DeePC framework to design control actions that are both effective and coherent with a PWA system's behavior, thus ensuring the controller's explainability.
| Comments: | 16 pages |
| Subjects: | Systems and Control (eess.SY) |
| Cite as: | arXiv:2605.23524 [eess.SY] |
| (or arXiv:2605.23524v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23524 arXiv-issued DOI via DataCite (pending registration) |
From: Gianluca Giacomelli [view email]
[v1]
Fri, 22 May 2026 11:40:32 UTC (548 KB)
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