Imperial College London
Portrait Picture

ProfessorEricKerrigan

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Control and Optimization
 
 
 
//

Contact

 

+44 (0)20 7594 6343e.kerrigan Website

 
 
//

Assistant

 

Mrs Raluca Reynolds +44 (0)20 7594 6281

 
//

Location

 

1114Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{O'Dwyer:2023:10.1109/TCST.2022.3224330,
author = {O'Dwyer, E and Kerrigan, E and Falugi, P and Zagorowska, M 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 improved performance using segmented trajectories},
url = {http://dx.doi.org/10.1109/TCST.2022.3224330},
volume = {31},
year = {2023}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A class of data-driven control methods has recently emerged based on Willems' fundamental lemma. Such methods can ease the modelling burden in control design but can be sensitive to disturbances acting on the system under control. In this paper, we extend these methods to incorporate segmented prediction trajectories. The proposed segmentation enables longer prediction horizons to be used in the presence of unmeasured disturbance. Furthermore, a computation time reduction can be achieved through segmentation by exploiting the problem structure, with computation time scaling linearly with increasing horizon length. 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 computation time for the segmented formulation is approximately half that of an unsegmented formulation for a horizon of 100 samples. The method is then applied to a building energy management problem, using a detailed simulation environment, in which we seek to minimise the discomfort and energy of a 6-room apartment. With the segmented formulation, a 72% reduction in discomfort and 5% financial cost reduction is achieved, compared to an unsegmented formulation using a one-day-ahead prediction horizon.
AU  - O'Dwyer,E
AU  - Kerrigan,E
AU  - Falugi,P
AU  - Zagorowska,M
AU  - Shah,N
DO  - 10.1109/TCST.2022.3224330
EP  - 1365
PY  - 2023///
SN  - 1063-6536
SP  - 1355
TI  - Data-driven predictive control with 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
UR  - http://hdl.handle.net/10044/1/95254
VL  - 31
ER  -
Download