activity
20152024
most citedIdentifiability of linear dynamic networks

91 citations · 226 across the 18 of their papers we have counts for

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17 papers · 1 filter

eess.SY2024

Data-informativity conditions for structured linear systems with implications for dynamic networks

Paul M. J. Van den Hof, Shengling Shi, Stefanie J. M. Fonken +3

When estimating a single subsystem (module) in a linear dynamic network with a prediction error method, a data-informativity condition needs to be satisfied for arriving at a consi…

eess.SY2022

Learning linear modules in a dynamic network with missing node observations

Karthik R. Ramaswamy, Giulio Bottegal, Paul M. J. Van den Hof

In order to identify a system (module) embedded in a dynamic network, one has to formulate a multiple-input estimation problem that necessitates certain nodes to be measured and in…

eess.SY2022★ 5 cited

Excitation allocation for generic identifiability of linear dynamic networks with fixed modules

H. J. Dreef, S. Shi, X. Cheng +2

Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. The problem of allocating a minimal number of external signa…

eess.SY2021★ 10 cited

A scalable multi-step least squares method for network identification with unknown disturbance topology

Stefanie J. M. Fonken, Karthik R. Ramaswamy, Paul M. J. Van den Hof

Identification methods for dynamic networks typically require prior knowledge of the network and disturbance topology, and often rely on solving poorly scalable non-convex optimiza…

eess.SY2021★ 10 cited

Identification of diffusively coupled linear networks through structured polynomial models

E. M. M., Kivits, Paul M. J. Van den Hof

Physical dynamic networks most commonly consist of interconnections of physical components that can be described by diffusive couplings. These diffusive couplings imply that the ca…

eess.SY2021★ 4 cited

Guaranteed performance analysis and controller synthesis for interconnected linear systems from noisy input-state data

Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof

The increase in available data and complexity of dynamical systems has sparked the research on data-based system performance analysis and controller design. Recent approaches can g…

eess.SY2019★ 1 cited

Bayesian topology identification of linear dynamic networks

Shengling Shi, Giulio Bottegal, Paul M. J. Van den Hof

In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper,…

eess.SY2019★ 16 cited

Abstractions of linear dynamic networks for input selection in local module identification

Harm H. M. Weerts, Jonas Linder, Martin Enqvist +1

In abstractions of linear dynamic networks, selected node signals are removed from the network, while keeping the remaining node signals invariant. The topology and link dynamics,…

eess.SY2018

Local module identification in dynamic networks with correlated noise: the full input case

Paul M. J. Van den Hof, Karthik R. Ramaswamy, Arne G. Dankers +1

The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module…

eess.SY2018

A recursive estimation approach to distributed identification of large-scale multi-input-single-output FIR systems

Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof

The problem of identifying single modules in multiple-input-single-output (MISO) systems is considered. A novel approach to distributed identification of MISO finite impulse respon…

eess.SY2018

Local module identification in dynamic networks: do more inputs guarantee smaller variance?

M. Mohsin Siraj, Max G. Potters, Paul M. J. Van den Hof

Recent developments in science and engineering have motivated control systems to be considered as interconnected and networked systems. From a system identification point of view,…

eess.SY2018

Single module identifiability in linear dynamic networks

Harm Weerts, Paul M. J. Van den Hof, Arne Dankers

A recent development in data-driven modelling addresses the problem of identifying dynamic models of interconnected systems, represented as linear dynamic networks. For these netwo…

eess.SY2017★ 42 cited

Prediction error identification of linear dynamic networks with rank-reduced noise

Harm H. M. Weerts, Paul M. J. Van den Hof, Arne G. Dankers

Dynamic networks are interconnected dynamic systems with measured node signals and dynamic modules reflecting the links between the nodes. We address the problem of \red{identifyin…

eess.SY2017★ 12 cited

Identification in Dynamic Networks

Paul M. J. Van den Hof, Arne G. Dankers, Harm H. M. Weerts

System identification is a common tool for estimating (linear) plant models as a basis for model-based predictive control and optimization. The current challenges in process indust…

eess.SY2016★ 91 cited

Identifiability of linear dynamic networks

Harm H. M. Weerts, Paul M. J. Van den Hof, Arne G. Dankers

Dynamic networks are structured interconnections of dynamical systems (modules) driven by external excitation and disturbance signals. In order to identify their dynamical properti…

eess.SY2015★ 3 cited

Observer-based correct-by-design controller synthesis

Sofie Haesaert, Paul M. J. Van den Hof, Alessandro Abate

Current state-of-the-art correct-by-design controllers are designed for full-state measurable systems. This work first extends the applicability of correct-by-design controllers to…

eess.SY2015

Data-driven and Model-based Verification: a Bayesian Identification Approach

Sofie Haesaert, Paul M. J. Van den Hof, Alessandro Abate

This work develops a measurement-driven and model-based formal verification approach, applicable to systems with partly unknown dynamics. We provide a principled method, grounded o…