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Noise-induced limitations to the scalability of distributed integral control
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-8975-1801
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0003-1835-2963
2019 (English)In: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 130, p. 23-31Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 130, p. 23-31
Keywords [en]
Fundamental limitations, Large-scale systems, Networked control systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262554DOI: 10.1016/j.sysconle.2019.06.005ISI: 000478703200004Scopus ID: 2-s2.0-85068851331OAI: oai:DiVA.org:kth-262554DiVA, id: diva2:1361869
Note

QC 20191017

Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-12-20Bibliographically approved

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Tegling, EmmaSandberg, Henrik

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