Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories

Edward O’Dwyer, Eric C. Kerrigan, Paola Falugi, Marta Zagorowska, Nilay Shah

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1355-1365
Number of pages11
JournalIEEE Transactions on Control Systems Technology
Volume31
Issue number3
DOIs
Publication statusPublished - 2023
Externally publishedYes

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