Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories

• Published in: IEEE transactions on control systems technology 2023-05, Vol.31 (3), p.1-0
• Main Authors: O'Dwyer, Edward, Kerrigan, Eric C., Falugi, Paola, Zagorowska, Marta, Shah, Nilay
• Format: Article
• Language: English
• Subjects: Building energy management; Control methods; Data models; Data structures; data-driven predictive control; Energy management; Error reduction; Linear systems; optimal control; Performance degradation; Performance prediction; Predictive control; Predictive models; Segmentation; Tracking errors; Training data; Trajectory; Willems’ fundamental lemma
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2022.3224330

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.