Publications

103 entries « 1 of 3 »

2024

D Liang: Frequency domain identification in dynamic networks for in-circuit testing. Eindhoven University of Technology, 2024, (MSc Graduation report). (Type: Masters Thesis | Links)
E M M Kivits: Modelling and identification of physical linear networks. Eindhoven University of Technology, 2024. (Type: PhD Thesis | Links)

2023

D Nikitas: Digital Twin for Diagnostics of Wafer Scanners. Eindhoven University of Technology, 2023, (MSc Graduation report). (Type: Masters Thesis | )
Y Shi: Fault detection and diagnosis using the dynamic network framework. Eindhoven University of Technology, 2023, (MSc Graduation report). (Type: Masters Thesis | Links)
T R V Steentjes, M Lazar, P M J Van den Hof: On a canonical distributed controller in the behavioral framework. Systems & Control Letters, 179 (105581), 2023. (Type: Journal Article | Links)
E E M Kivits, P M J Van den Hof: Identification of diffusively coupled linear networks through structured polynomial models. IEEE Trans. Automatic Control, 68 (6), pp. 3513-3528, 2023. (Type: Journal Article | Links)
S Shi, X Cheng, P M J Van den Hof: Single module identifiability in linear dynamic networks with partial excitation and measurement. IEEE Trans. Automatic Control, 68 (1), pp. 285-300, 2023. (Type: Journal Article | Links)
X Bombois, K Colin, P M J Van den Hof, H Hjalmarsson: On the informativity of direct identification experiments in dynamical networks. Automatica, 148 (110742), 2023. (Type: Journal Article | Links)
X Cheng, S Shi, I Lestas, P M J Van den Hof: A necessary condition for network identifiability with partial excitation and measurement. IEEE Trans. Automatic Control, 68 (11), pp. 6820-6827, 2023. (Type: Journal Article | Links)
Control Systems Group, Eindhoven University of Technology : SYSDYNET Toolbox for MATLAB, Version Beta 0.2.0. 2023. (Type: Journal Article | Links)
S J M Fonken, K R Ramaswamy, P M J Van den Hof: Local identification in dynamic networks using a multi-step least squares method. Proc. 62th IEEE Conf. on Decision and Control (CDC), pp. 431-436, IEEE Singapore, 2023. (Type: Inproceedings | Links)
P M J Van den Hof, K R Ramaswamy, S J M Fonken: Integrating data-informativity conditions in predictor models for single module identification in dynamic networks. IFAC-PapersOnLine, 56-2 , pp. 2377-2382, 2023, (Proc. 22nd IFAC World Congress, 9-14 July, 2023, Yokohama, Japan). (Type: Journal Article | Links)
S Shi, X Cheng, B De Schutter, P M J Van den Hof: Signal selection for local module identification in linear dynamic networks: A graphical approach. IFAC-PapersOnLine, 56-2 , pp. 2407-2412, 2023, (Proc. 22nd IFAC World Congress, 9-14 July, 2023, Yokohama, Japan). (Type: Journal Article | Links)
E M M Kivits, P M J Van den Hof: Identifiability of diffusively coupled linear networks with partial instrumentation. IFAC-PapersOnLine, 56-2 , pp. 2395-2400, 2023, (Proc. 22nd IFAC World Congress, 9-14 July, 2023, Yokohama, Japan). (Type: Journal Article | Links)

2022

K R Ramaswamy, P Z Csurcsia, J Schoukens, P M J Van den Hof: A frequency domain approach for local module identification in dynamic networks. Automatica, 142 (110370), 2022. (Type: Journal Article | Links)
S J M Fonken, K R Ramaswamy, P M J Van den Hof: A scalable multi-step least squares method for network identification with unknown disturbance topology. Automatica, 141 (110295), 2022. (Type: Journal Article | Links)
S Shi, X Cheng, P M J Van den Hof: Generic identifiability of subnetworks in a linear dynamic network: the full measurement case. Automatica, 137 (110093), 2022. (Type: Journal Article | Links)
X Cheng, S Shi, P M J Van den Hof: Allocation of excitation signals for generic identifiability of linear dynamic networks. IEEE Trans. Automatic Control, 67 (2), pp. 592-705, 2022. (Type: Journal Article | Links)
T M V Steentjes, M Lazar, P M J Van den Hof: On data-driven control: informativity of noisy input-output data with cross-covariance bounds. IEEE Control Systems Letters (L-CSS), 6 , pp. 2192-2197, 2022. (Type: Journal Article | Links)
K R Ramaswamy: A guide to learning modules in a dynamic network. Eindhoven University of Technology, 2022. (Type: PhD Thesis | Links)
H J Dreef, S Shi, X. Cheng, M C F Donkers, P M J Van den Hof: Excitation allocation for generic identifiability of linear dynamic networks with fixed modules. IEEE Control Systems Letters (L-CSS), 6 , pp. 2587-2592, 2022. (Type: Journal Article | Links)
T R V Steentjes: Data-driven methods for distributed control of interconnected linear systems. Eindhoven University of Technology, 2022. (Type: PhD Thesis | Links)
K R Ramaswamy, G Bottegal, P M J Van den Hof: Learning linear modules in a dynamic network with missing node observations. 2022, (Automatica, under review. ArXiv:2208.10995 [cs.sY]). (Type: Technical Report | Links)
T R V Steentjes, M Lazar, P M J Van den Hof: Informativity conditions for data-driven control based on input-state data and polyhedral cross-covariance noise bounds. Proc. 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2022), pp. 816-821, Bayreuth, Germany, 2022. (Type: Inproceedings | Links)
E M M Kivits, P M J Van den Hof: Local identification in diffusively coupled linear networks. Proc. 61th IEEE Conf. on Decision and Control (CDC), pp. 874-879, IEEE Cancun, Mexico, 2022. (Type: Inproceedings | Links)

2021

J B T Meijer: Improvements for In-Circuit Testing using RLC Network Identification. Eindhoven University of Technology, 2021, (MSc Graduation report). (Type: Masters Thesis | Links)
K R Ramaswamy, P M J Van den Hof: A local direct method for module identification in dynamic networks with correlated noise. IEEE Trans. Automatic Control, 66 (11), pp. 5237-5252, 2021. (Type: Journal Article | Links)
K R Ramaswamy, G Bottegal, P M J Van den Hof: Learning linear models in a dynamic network using regularized kernel-based methods. Automatica, 129 (109591), 2021. (Type: Journal Article | Links)
S Shi: Topological aspects of linear dynamic networks: identifiability and identification. Eindhoven University of Technology, 2021. (Type: PhD Thesis | Links)
T R V Steentjes, M Lazar, P M J Van den Hof: Scalable distributed H2 controller synthesis for interconnected linear discrete-time systems. IFAC-PapersOnLine, 54-9 , pp. 66-71, 2021, (Proc. 24th Intern. Symposium MTNS, Cambridge, UK). (Type: Journal Article | Links)
T R V Steentjes, M Lazar, P M J Van den Hof: H_infinity performance analysis an distributed controller synthesis for interconnected linear systems from noisy input-state data. Proc. 60th IEEE Conf. on Decision and Control (CDC), pp. 3723-3728, IEEE Austin, Texas, 2021. (Type: Inproceedings | Links)
V C Rajagopal, K R Ramaswamy, P M J Van den Hof: Learning local modules in dynamic networks without prior topology information. Proc. 60th IEEE Conf. on Decision and Control (CDC), pp. 840-845, IEEE Austin, Texas, 2021. (Type: Inproceedings | Links)
H J Dreef, M C F Donkers, P M J Van den Hof: Identifiability of linear dynamic networks through switching modules. IFAC-PapersOnLine, 54-7 , pp. 37-42, 2021, (Proc. 19th IFAC Symposium on System Identification - Learning Models for Decision and Control). (Type: Journal Article | Links)
S Shi, X Cheng, P M J Van den Hof: Exploiting unmeasured disturbance signals in identifiability of linear dynamic networks with partial measurement and partial excitation. Prepr. 19th IFAC Symposium on System Identification - Learning Models for Decision and Control, pp. 264-267, Padova, Italy, 2021, (Extended abstract). (Type: Inproceedings | Links)
T R V Steentjes, P M J Van den Hof, M Lazar: Handling unmeasured disturbances in data-driven distributed control with virtual reference feedback tuning. IFAC-PapersOnLine, 54-7 , pp. 204-209, 2021, (Proc. 19th IFAC Symposium on System Identification - Learning Models for Decision and Control). (Type: Journal Article | Links)
P M J Van den Hof, K R Ramaswamy: Learning local models in dynamic networks. Proc. Machine Learning Research, 144 , pp. 176-188, 2021. (Type: Journal Article | Links)
X Cheng, S Shi, P M J Van den Hof: Identifiability in dynamic acyclic networks with partial excitation and measurement. Prepr. 2021 European Control Conference, IEEE Rotterdam, The Netherlands, 2021, (Extended abstract). (Type: Conference | Links)
T R V Steentjes, M Lazar, P M J Van den Hof: Data-driven distributed controller synthesis in the presence of noise: an optimal controller identification approach. Proc. 2021 European Control Conference, pp. 2358-2363, IEEE Rotterdam, The Netherlands, 2021. (Type: Inproceedings | Links)
Anupama: Data-driven modelling and decentralized H_2 control of power networks. Eindhoven University of Technology, 2021, (MSc Graduation report). (Type: Masters Thesis | Links)

2020

S J M Fonken: Multi-step scalable least squares method for network identification with unknown noise topology. Eindhoven University of Technology, 2020, (MSc Graduation report). (Type: Masters Thesis | Links)
R J C van Esch, S Shi, A Bernas, Zinger Z., A P Aldenkamp, P M J Van den Hof: A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect. Computers in Biology and Medicine, 2020. (Type: Journal Article | Links)
V C Rajagopal: Learning local modules in dynamic networks without prior topology information. Eindhoven University of Technology, 2020, (MSc Graduation report). (Type: Masters Thesis | Links)
H H M Weerts, J Linder, M Enqvist, P M J Van den Hof: Abstractions of linear dynamic networks for input selection in local module identification. Automatica, 117 (108975), 2020. (Type: Journal Article | Links)
P M J Van den Hof, K R Ramaswamy, S Shi, H J Dreef: Identifiability and data-informativity for single module identification in dynamic networks.. 2020, (Accepted as extended abstract in MTNS2020, but unpublished). (Type: Technical Report | Links)
S Shi, X Cheng, P M J Van den Hof: Excitation allocation for generic identifiability of a single module in dynamic networks: A graphic approach. IFAC-PapersOnLine, 53-2 , pp. 40-45, 2020, (Proc. 21st IFAC World Congress, Berlin, Germany). (Type: Journal Article | Links)
S Fonken, M Ferizbegovic, H Hjalmarsson: Consistent identification of dynamic networks subject to white noise using weighted null-space fitting. IFAC-PapersOnLine, 53-2 , pp. 46-51, 2020, (Proc. 21st IFAC World Congress, Berlin, Germany). (Type: Journal Article | Links)
P M J Van den Hof, K R Ramaswamy: Single module identification in dynamic networks - the current status. Prepr. 21st IFAC World Congress, pp. 52-55, Berlin, Germany, 2020, (Extended abstract). (Type: Inproceedings | Links)
P M J Van den Hof, K R Ramaswamy: Path-based data-informativity conditions for single module identification in dynamic networks. Eindhoven University of Technology 2020, (Extended report version of a paper presented at the 59th IEEE Conf. Decision and Control, Jeju Island, Republic of Korea, 15-18 December 2020. ). (Type: Technical Report | Links)
P M J Van den Hof, K R Ramaswamy: Path-based data-informativity conditions for single module identification in dynamic networks. Proc. 59th IEEE Conf. on Decision and Control (CDC), pp. 4354-4359, IEEE Jeju Island, Republic of Korea, 2020. (Type: Inproceedings | Links)
T R V Steentjes, M Lazar, P M J Van den Hof: Data-driven distributed control via virtual reference feedback tuning. Proc. 59th IEEE Conf. on Decision and Control (CDC), pp. 1804-1809, IEEE Jeju Island, Republic of Korea, 2020. (Type: Inproceedings | Links)
103 entries « 1 of 3 »