Dario Piga

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Citations	3227	2597
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Alberto BemporadProfessor of Control Systems, IMT Lucca, ItalyVerified email at imtlucca.it
Loris RovedaIDSIA-SUPSI/USIVerified email at idsia.ch
Diego RegrutoAssociate Professor of Control Theory, Politecnico di TorinoVerified email at polito.it
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
Simone FormentinAssociate Professor, Politecnico di MilanoVerified email at polimi.it
Andrea CastellettiProfessor, Politecnico di MilanoVerified email at polimi.it
Andrea CominolaAssistant Professor | Einstein Center Digital Future - Technische Universität BerlinVerified email at tu-berlin.de
Alessio BenavoliAssociate Professor in Statistics, SCSS, Trinity College DublinVerified email at tcd.ie
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
Sergio Matteo Savaresipolitecnico milanoVerified email at polimi.it
Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, StanfordVerified email at stanford.edu
John LataireVrije Universiteit BrusselVerified email at vub.ac.be
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	305	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	160	2017
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	159	2021
High-altitude wind power generation L Fagiano, M Milanese, D Piga IEEE Transactions on Energy Conversion 25 (1), 168-180, 2009	132	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
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	106	2019
Direct data-driven control of constrained systems D Piga, S Formentin, A Bemporad IEEE Transactions on Control Systems Technology 26 (4), 1422-1429, 2017	104	2017
Piecewise affine regression via recursive multiple least squares and multicategory discrimination V Breschi, D Piga, A Bemporad Automatica 73, 155-162, 2016	92	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	91	2015
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	82	2011
Direct learning of LPV controllers from data S Formentin, D Piga, R Tóth, SM Savaresi Automatica 65, 98-110, 2016	78	2016
Fitting jump models A Bemporad, V Breschi, D Piga, SP Boyd Automatica 96, 11-21, 2018	52	2018
LPV system identification under noise corrupted scheduling and output signal observations D Piga, P Cox, R Tóth, V Laurain Automatica 53, 329-338, 2015	50	2015
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	49	2021
An instrumental least squares support vector machine for nonlinear system identification V Laurain, R Tóth, D Piga, WX Zheng Automatica 54, 340-347, 2015	45	2015
Continuous-time system identification with neural networks: Model structures and fitting criteria M Forgione, D Piga European Journal of Control 59, 69-81, 2021	43	2021
Set-membership LPV model identification of vehicle lateral dynamics V Cerone, D Piga, D Regruto Automatica 47 (8), 1794-1799, 2011	42	2011
Robot control parameters auto-tuning in trajectory tracking applications L Roveda, M Forgione, D Piga Control Engineering Practice 101, 104488, 2020	40	2020
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	40	2020
A bias-correction method for closed-loop identification of Linear Parameter-Varying systems M Mejari, D Piga, A Bemporad Automatica 87, 128-141, 2018	37	2018

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