Publications

84 entries « 1 of 2 »

2023

E E M Kivits, P M J Van den Hof: Identification of diffusively coupled linear networks through structured polynomial models. IEEE Trans. Automatic Control, 73 , 2023, (To appear). (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 , 2023, (To appear). (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)
Fonken S. J. M., Ramaswamy K. R., 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)
Dreef H. J., Shi S., Cheng X., Donkers M. C. F., 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)

2021

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)

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)
V C Rajagopal, K R Ramaswamy, P M J Van den Hof: A regularized kernel-based method for learning a module in a dynamic network with correlated noise. Proc. 59th IEEE Conf. on Decision and Control (CDC), pp. 4348-4353, IEEE Jeju Island, Republic of Korea, 2020. (Type: Inproceedings | Links)
T R V Steentjes, M Lazar, P M J Van den Hof: Scalable distributed and decentralized H_2 controller synthesis for interconnected linear discrete-time systems. 2020, (ArXiv:2001.04875 [cs.sY]. Extended version of paper accepted for MTNS 2020.). (Type: Technical Report | Links)

2019

V C Rajagopal: An iterative algorithm for learning dynamic networks with correlated noise. Eindhoven University of Technology, 2019, (Internship Report). (Type: Masters Thesis | Links)
R van Esch: Topology detection in brain networks. Eindhoven University of Technology, 2019. (Type: Masters Thesis | Links)
X Chen: Centralized and distributed identified model based predictive control for museum Hermitage Amsterdam. Eindhoven University of Technology, 2019. (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. Eindhoven University of Technology 2019, (arXiv:1901.00348 [cs.SY]). (Type: Technical Report | Links)
S Shi, G Bottegal, P M J Van den Hof: Bayesian topology identification of linear dynamic networks. Proc. 2019 European Control Conference, pp. 2814-2819, Napels, Italy, 2019. (Type: Inproceedings | Links)
K R Ramaswamy, P M J Van den Hof: A local direct method for module identification in dynamic networks with correlated noise. Eindhoven University of Technology 2019, (ArXiv:1908.00976 [cs.sY]). (Type: Technical Report | Links)
Cheng X., S Shi, P M J Van den Hof: Allocation of excitation signals for generic identifiability of linear dynamic networks. Eindhoven University of Technology 2019, (ArXiv:1910.04525 [math.OC]). (Type: Technical Report | Links)
K R Ramaswamy, P M J Van den Hof, Dankers A G.: Generalized sensing and actuation schemes for local module identification in dynamic networks. Proc. 58th IEEE Conf. on Decision and Control (CDC), pp. 5519-5524, IEEE Nice, France, 2019. (Type: Inproceedings | Links)
P M J Van den Hof, K R Ramaswamy, A G Dankers, G Bottegal: Local module identification in dynamic networks with correlated noise: the full input case. Proc. 58th IEEE Conf. on Decision and Control (CDC), pp. 5494-5499, IEEE Nice, France, 2019. (Type: Inproceedings | Links)
X Cheng, S Shi, P M J Van den Hof: Allocation of excitation signals for generic identifiability of dynamic networks. Proc. 58th IEEE Conf. on Decision and Control (CDC), pp. 5507-5512, IEEE Nice, France, 2019. (Type: Inproceedings | Links)
E M M Kivits, P M J Van den Hof: A dynamic network approach to identification of physical systems. Proc. 58th IEEE Conf. on Decision and Control (CDC), pp. 4533-4538, IEEE Nice, France, 2019. (Type: Inproceedings | Links)

2018

H H M Weerts, P M J Van den Hof, A G Dankers: Prediction error identification of linear dynamic networks with rank-reduced noise. Automatica, 98 , pp. 256-268, 2018. (Type: Journal Article | Links)
Yang Song: Identification of continuous-time ARX-Models subject to missing data. Eindhoven University of Technology, 2018. (Type: Masters Thesis | Links)
N Everitt, G Bottegal, H Hjalmarsson: An emperical Bayes approach to identification of modules in dynamic networks. Automatica, 91 , pp. 144-151, 2018. (Type: Journal Article | Links)
H H M Weerts, P M J Van den Hof, A G Dankers: Identifiability of linear dynamic networks. Automatica, 89 , pp. 247-258, 2018. (Type: Journal Article | Links)
P M J Van den Hof, A G Dankers, Weerts H H M: Identification in dynamic networks. Computers & Chemical Engineering, 109 , pp. 23-29, 2018. (Type: Journal Article | Links)
H H M Weerts, M Galrinho, G Bottegal, H Hjalmarsson, P M J Van den Hof: A sequential least squares algorithm for ARMAX dynamic network identification. IFAC-PapersOnLine, 51-15 , pp. 844-849, 2018, (Proc. 18th IFAC Symp. System Identification). (Type: Journal Article | Links)
84 entries « 1 of 2 »