Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories

Article


O’Dwyer, E., Kerrigan, E. C., Falugi, P., Zagorowska, M. and Shah, N. 2022. Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories. IEEE Transactions on Control Systems Technology . 31 (3), pp. 1355 - 1365. https://doi.org/10.1109/TCST.2022.3224330
AuthorsO’Dwyer, E., Kerrigan, E. C., Falugi, P., Zagorowska, M. and Shah, N.
Abstract

A class of data-driven control methods has recently emerged based on Willems’ fundamental lemma. Such methods can ease the modeling burden in control design but can be sensitive to disturbances acting on the system under control. In this article, we propose a restructuring of the problem to incorporate segmented prediction trajectories. The proposed segmentation leads to reduced tracking error for longer prediction horizons in the presence of unmeasured disturbance and noise when compared with an unsegmented formulation. The performance characteristics are illustrated in a set-point tracking case study in which the segmented formulation enables more consistent performance over a wide range of prediction horizons. The method is then applied to a building energy management problem using a detailed simulation environment. The case studies show that good tracking performance is achieved for a range of horizon choices, whereas performance degrades with longer horizons without segmentation.

JournalIEEE Transactions on Control Systems Technology
Journal citation31 (3), pp. 1355 - 1365
ISSN1063-6536
Year2022
PublisherIEEE
Accepted author manuscript
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1109/TCST.2022.3224330
Publication dates
Print01 Dec 2022
Publication process dates
Deposited30 Jun 2023
FunderEngineering and Physical Sciences Research Council (EPSRC)
Copyright holder© 2022, The Author(s)
Copyright informationFor the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) license to any Accepted Manuscript version arising
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