Dario Piga

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Citations	2577	1949
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Alberto BemporadProfessor of Control Systems, IMT Lucca, ItalyVerified email at imtlucca.it
Diego RegrutoAssociate Professor of Control Theory, Politecnico di TorinoVerified email at polito.it
Loris RovedaIDSIA-SUPSI/USIVerified email at idsia.ch
Roland TothAssociate Professor, Eindhoven University of TechnologyVerified email at tue.nl
Marco ForgioneDalle Molle Institute for ArtificiaI Intelligence, SUPSI-USI, LuganoVerified email at idsia.ch
Manas MejariIDSIA Dalle Molle Institute for Artificial Intelligence, SwitzerlandVerified email at idsia.ch
Andrea CastellettiProfessor, Politecnico di MilanoVerified email at polimi.it
Andrea CominolaAssistant Professor | Einstein Center Digital Future - Technische Universität BerlinVerified email at tu-berlin.de
Simone FormentinAssociate Professor, Politecnico di MilanoVerified email at polimi.it
Matteo GiulianiAssistant Professor, Politecnico di MilanoVerified email at polimi.it
Lorenzo FagianoAssociate Professor, Politecnico di MilanoVerified email at polimi.it
Andrea-Emilio RizzoliIDSIA - USI/SUPSI Istituto Dalle Molle di Studi sull'Intelligenza ArtificialeVerified email at idsia.ch
Alessio BenavoliAssociate Professor in Statistics, SCSS, Trinity College DublinVerified email at tcd.ie
Sergio Matteo Savaresipolitecnico milanoVerified email at polimi.it
John LataireVrije Universiteit BrusselVerified email at vub.ac.be
Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, StanfordVerified email at stanford.edu
Jean B. LasserreLAAS-CNRS, Toulouse France, ANITIVerified email at laas.fr
Marco ZaffalonIDSIA and Artificialy.comVerified email at idsia.ch
Rik PintelonProfessor in electrical engineering, Vrije Universiteit Brussel, dept. ELECVerified email at vub.ac.be

Dario Piga

Senior Researcher at IDSIA - Dalle Molle Institute for ArtificiaI Intelligence, SUPSI-USI, Lugano

Verified email at supsi.ch - Homepage

System Identification Machine Learning Control theory


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Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review A Cominola, M Giuliani, D Piga, A Castelletti, AE Rizzoli Environmental Modelling & Software 72, 198-214, 2015	274	2015
A hybrid signature-based iterative disaggregation algorithm for non-intrusive load monitoring A Cominola, M Giuliani, D Piga, A Castelletti, AE Rizzoli Applied energy 185, 331-344, 2017	143	2017
High-altitude wind power generation L Fagiano, M Milanese, D Piga IEEE Transactions on Energy Conversion 25 (1), 168-180, 2009	127	2009
Optimization of airborne wind energy generators L Fagiano, M Milanese, D Piga International Journal of robust and nonlinear control 22 (18), 2055-2083, 2012	126	2012
Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets F D'Ascenzo, O De Filippo, G Gallone, G Mittone, MA Deriu, ... The Lancet 397 (10270), 199-207, 2021	90	2021
Piecewise affine regression via recursive multiple least squares and multicategory discrimination V Breschi, D Piga, A Bemporad Automatica 73, 155-162, 2016	86	2016
Sparse optimization for automated energy end use disaggregation D Piga, A Cominola, M Giuliani, A Castelletti, AE Rizzoli IEEE Transactions on Control Systems Technology 24 (3), 1044-1051, 2015	84	2015
Direct data-driven control of constrained systems D Piga, S Formentin, A Bemporad IEEE Transactions on Control Systems Technology 26 (4), 1422-1429, 2017	81	2017
Set-membership error-in-variables identification through convex relaxation techniques V Cerone, D Piga, D Regruto IEEE Transactions on Automatic Control 57 (2), 517-522, 2011	77	2011
Performance-oriented model learning for data-driven MPC design D Piga, M Forgione, S Formentin, A Bemporad IEEE control systems letters 3 (3), 577-582, 2019	74	2019
Direct learning of LPV controllers from data S Formentin, D Piga, R Tóth, SM Savaresi Automatica 65, 98-110, 2016	69	2016
LPV system identification under noise corrupted scheduling and output signal observations D Piga, P Cox, R Toth, V Laurain Automatica 53, 329-338, 2015	45	2015
An instrumental least squares support vector machine for nonlinear system identification V Laurain, R Tóth, D Piga, WX Zheng Automatica 54, 340-347, 2015	42	2015
Fitting jump models A Bemporad, V Breschi, D Piga, SP Boyd Automatica 96, 11-21, 2018	39	2018
Set-membership LPV model identification of vehicle lateral dynamics V Cerone, D Piga, D Regruto Automatica 47 (8), 1794-1799, 2011	38	2011
Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian Optimization L Roveda, M Magni, M Cantoni, D Piga, G Bucca Robotics and Autonomous Systems 136, 103711, 2021	36	2021
Robot control parameters auto-tuning in trajectory tracking applications L Roveda, M Forgione, D Piga Control Engineering Practice 101, 104488, 2020	34	2020
A unified framework for solving a general class of conditional and robust set-membership estimation problems V Cerone, JB Lasserre, D Piga, D Regruto IEEE Transactions on Automatic Control 59 (11), 2897-2909, 2014	34	2014
An SDP approach for ℓ0-minimization: Application to ARX model segmentation D Piga, R Tóth Automatica 49 (12), 3646-3653, 2013	31	2013
Torque vectoring for high-performance electric vehicles: an efficient mpc calibration A Lucchini, S Formentin, M Corno, D Piga, SM Savaresi IEEE Control Systems Letters 4 (3), 725-730, 2020	30	2020

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