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Damiano Varagnolo
Damiano Varagnolo
Department of Engineering Cybernetics, NTNU - Norwegian University of Science and Technology
Verified email at ntnu.no - Homepage
Title
Cited by
Cited by
Year
Newton-Raphson consensus for distributed convex optimization
D Varagnolo, F Zanella, A Cenedese, G Pillonetto, L Schenato
IEEE Transactions on Automatic Control 61 (4), 994-1009, 2015
1972015
Newton-Raphson consensus for distributed convex optimization
F Zanella, D Varagnolo, A Cenedese, G Pillonetto, L Schenato
2011 50th IEEE Conference on Decision and Control and European Control …, 2011
1152011
Consensus‐based distributed sensor calibration and least‐square parameter identification in WSNs
S Bolognani, S Del Favero, L Schenato, D Varagnolo
International Journal of Robust and Nonlinear Control: IFAC‐Affiliated …, 2010
932010
Estimation of building occupancy levels through environmental signals deconvolution
A Ebadat, G Bottegal, D Varagnolo, B Wahlberg, KH Johansson
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient …, 2013
912013
A scenario-based predictive control approach to building HVAC management systems
A Parisio, M Molinari, D Varagnolo, KH Johansson
2013 IEEE International Conference on Automation Science and Engineering …, 2013
702013
Implementation of a Scenario-based MPC for HVAC Systems: an Experimental Case Study
A Parisio, D Varagnolo, M Molinari, G Pattarello, L Fabietti, KH Johansson
The 19th World Congress of the International Federation of Automatic Control, 2014
672014
Asynchronous Newton-Raphson consensus for distributed convex optimization
F Zanella, D Varagnolo, A Cenedese, G Pillonetto, L Schenato
IFAC Proceedings Volumes 45 (26), 133-138, 2012
622012
Control of HVAC systems via scenario-based explicit MPC
A Parisio, L Fabietti, M Molinari, D Varagnolo, KH Johansson
53rd IEEE conference on decision and control, 5201-5207, 2014
602014
Distributed Cardinality Estimation in Anonymous Networks
D Varagnolo, G Pillonetto, L Schenato
IEEE Transactions on Automatic Control 59 (3), 645 - 659, 2014
542014
Regularized deconvolution-based approaches for estimating room occupancies
A Ebadat, G Bottegal, D Varagnolo, B Wahlberg, KH Johansson
IEEE Transactions on Automation Science and Engineering 12 (4), 1157-1168, 2015
492015
Distributed statistical estimation of the number of nodes in sensor networks
D Varagnolo, G Pillonetto, L Schenato
49th IEEE Conference on Decision and Control (CDC), 1498-1503, 2010
492010
Randomized model predictive control for HVAC systems
A Parisio, D Varagnolo, D Risberg, G Pattarello, M Molinari, ...
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient …, 2013
422013
Optimal contracts for wind power producers in electricity markets
E Bitar, A Giani, R Rajagopal, D Varagnolo, P Khargonekar, K Poolla, ...
49th IEEE Conference on Decision and Control (CDC), 1919-1926, 2010
402010
Distributed estimation of diameter, radius and eccentricities in anonymous networks
F Garin, D Varagnolo, KH Johansson
IFAC Proceedings Volumes 45 (26), 13-18, 2012
382012
Detecting broken rotor bars in induction motors with model-based support vector classifiers
MO Mustafa, D Varagnolo, G Nikolakopoulos, T Gustafsson
Control Engineering Practice 52, 15-23, 2016
362016
Distributed size estimation of dynamic anonymous networks
H Terelius, D Varagnolo, KH Johansson
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 5221-5227, 2012
342012
Analysis of Newton-Raphson consensus for multi-agent convex optimization under asynchronous and lossy communications
R Carli, G Notarstefano, L Schenato, D Varagnolo
2015 54th IEEE Conference on Decision and Control (CDC), 418-424, 2015
322015
Multiagent Newton–Raphson optimization over lossy networks
N Bof, R Carli, G Notarstefano, L Schenato, D Varagnolo
IEEE Transactions on Automatic Control 64 (7), 2983-2990, 2018
292018
Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning
A Heiberg, TN Larsen, E Meyer, A Rasheed, O San, D Varagnolo
Neural Networks 152, 17-33, 2022
262022
Comparing deep reinforcement learning algorithms’ ability to safely navigate challenging waters
TN Larsen, HØ Teigen, T Laache, D Varagnolo, A Rasheed
Frontiers in Robotics and AI 8, 738113, 2021
262021
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