Noise-induced limitations to the scalability of distributed integral control
(2019) In Systems and Control Letters 130. p.23-31- Abstract
We study performance limitations of distributed feedback control in large-scale networked dynamical systems. Specifically, we address the question of how the performance of distributed integral control is affected by measurement noise. We consider second-order consensus-like problems modeled over a toric lattice network, and study asymptotic scalings (in network size) of H2 performance metrics that quantify the variance of nodal state fluctuations. While previous studies have shown that distributed integral control fundamentally improves these performance scalings compared to distributed proportional feedback control, our results show that an explicit inclusion of measurement noise leads to the opposite conclusion. The... (More)
We study performance limitations of distributed feedback control in large-scale networked dynamical systems. Specifically, we address the question of how the performance of distributed integral control is affected by measurement noise. We consider second-order consensus-like problems modeled over a toric lattice network, and study asymptotic scalings (in network size) of H2 performance metrics that quantify the variance of nodal state fluctuations. While previous studies have shown that distributed integral control fundamentally improves these performance scalings compared to distributed proportional feedback control, our results show that an explicit inclusion of measurement noise leads to the opposite conclusion. The noise's impact on performance is shown to decrease with an increased inter-nodal alignment of the local integral states. However, even though the controller can be tuned for acceptable performance for any given network size, performance will degrade as the network grows, limiting the scalability of any such controller tuning. In particular, the requirement for inter-nodal alignment increases with network size. We show that this may in practice imply that very large and sparse networks will require any integral control to be centralized, rather than distributed. In this case, the best-achievable performance scaling, which is shown to be that of proportional feedback control, is retrieved.
(Less)
- author
- Tegling, Emma LU and Sandberg, Henrik LU
- publishing date
- 2019-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Fundamental limitations, Large-scale systems, Networked control systems
- in
- Systems and Control Letters
- volume
- 130
- pages
- 9 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85068851331
- ISSN
- 0167-6911
- DOI
- 10.1016/j.sysconle.2019.06.005
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2019
- id
- 36e086fa-07a3-4d0c-afc0-c00ce8e28dd1
- date added to LUP
- 2021-11-24 09:52:27
- date last changed
- 2022-04-27 06:05:43
@article{36e086fa-07a3-4d0c-afc0-c00ce8e28dd1,
abstract = {{<p>We study performance limitations of distributed feedback control in large-scale networked dynamical systems. Specifically, we address the question of how the performance of distributed integral control is affected by measurement noise. We consider second-order consensus-like problems modeled over a toric lattice network, and study asymptotic scalings (in network size) of H<sub>2</sub> performance metrics that quantify the variance of nodal state fluctuations. While previous studies have shown that distributed integral control fundamentally improves these performance scalings compared to distributed proportional feedback control, our results show that an explicit inclusion of measurement noise leads to the opposite conclusion. The noise's impact on performance is shown to decrease with an increased inter-nodal alignment of the local integral states. However, even though the controller can be tuned for acceptable performance for any given network size, performance will degrade as the network grows, limiting the scalability of any such controller tuning. In particular, the requirement for inter-nodal alignment increases with network size. We show that this may in practice imply that very large and sparse networks will require any integral control to be centralized, rather than distributed. In this case, the best-achievable performance scaling, which is shown to be that of proportional feedback control, is retrieved.</p>}},
author = {{Tegling, Emma and Sandberg, Henrik}},
issn = {{0167-6911}},
keywords = {{Fundamental limitations; Large-scale systems; Networked control systems}},
language = {{eng}},
pages = {{23--31}},
publisher = {{Elsevier}},
series = {{Systems and Control Letters}},
title = {{Noise-induced limitations to the scalability of distributed integral control}},
url = {{http://dx.doi.org/10.1016/j.sysconle.2019.06.005}},
doi = {{10.1016/j.sysconle.2019.06.005}},
volume = {{130}},
year = {{2019}},
}