91 citations · 226 across the 18 of their papers we have counts for
20 papers
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…
On a Canonical Distributed Controller in the Behavioral Framework
Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof
Control in a classical transfer function or state-space setting typically views a controller as a signal processor: sensor outputs are mapped to actuator inputs. In behavioral syst…
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…
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…
Identifiability in Dynamic Acyclic Networks with Partial Excitations and Measurements
Xiaodong Cheng, Shengling Shi, Ioannis Lestas +1
This paper deals with dynamic networks in which the causality relations between the vertex signals are represented by linear time-invariant transfer functions (modules). Considerin…
On data-driven control: informativity of noisy input-output data with cross-covariance bounds
Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof
In this paper we develop new data informativity based controller synthesis methods that extend existing frameworks in two relevant directions: a more general noise characterization…
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…
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…
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…
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,…
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,…
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…
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…
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,…
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…
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…
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…
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…
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…
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…