In this section
@article{O'Dwyer:2022:10.1109/TCST.2022.3224330,
author = {O'Dwyer, E and Kerrigan, EC and Falugi, P and Zagorowska, MA and Shah, N},
doi = {10.1109/TCST.2022.3224330},
journal = {IEEE Transactions on Control Systems Technology},
pages = {1355--1365},
title = {Data-driven predictive control with reduced computational effort and improved performance using segmented trajectories},
url = {http://dx.doi.org/10.1109/TCST.2022.3224330},
volume = {31},
year = {2022}
}
TY - JOUR
AB - 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.
AU - O'Dwyer,E
AU - Kerrigan,EC
AU - Falugi,P
AU - Zagorowska,MA
AU - Shah,N
DO - 10.1109/TCST.2022.3224330
EP - 1365
PY - 2022///
SN - 1063-6536
SP - 1355
TI - Data-driven predictive control with reduced computational effort and improved performance using segmented trajectories
T2 - IEEE Transactions on Control Systems Technology
UR - http://dx.doi.org/10.1109/TCST.2022.3224330
UR - http://arxiv.org/abs/2108.10753v1
VL - 31
ER -