Marco Forgione

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Dario PigaSenior Researcher at IDSIA - Dalle Molle Institute for ArtificiaI Intelligence, SUPSI-USI, LuganoDirección de correo verificada de supsi.ch
Paul M.J. Van den HofProfessor Eindhoven University of TechnologyDirección de correo verificada de tue.nl
Xavier BomboisCNRS Research Director, Laboratoire Ampère, Ecole Centrale de LyonDirección de correo verificada de ec-lyon.fr
Loris RovedaAssociate Professor @ PoliMi; Senior Researcher @ IDSIA-SUPSI/USIDirección de correo verificada de polimi.it
Manas MejariIDSIA Dalle Molle Institute for Artificial Intelligence, SwitzerlandDirección de correo verificada de idsia.ch
Håkan HjalmarssonCentre for Advanced Bioproduction, Linnaeus Centre ACCESS, School of EE and CSDirección de correo verificada de kth.se
Ali MesbahUniversity of California, BerkeleyDirección de correo verificada de berkeley.edu
Simone FormentinAssociate Professor, Politecnico di MilanoDirección de correo verificada de polimi.it
Alberto BemporadProfessor of Control Systems, IMT Lucca, ItalyDirección de correo verificada de imtlucca.it
Herman J.M. KramerAssociate professor, Delft University of TechnologyDirección de correo verificada de tudelft.nl
Bojan MavkovAssociate Professor, Université Côte d'AzurDirección de correo verificada de i3s.unice.fr
Magni LaloProfessor of Automatic Control at University of PaviaDirección de correo verificada de unipv.it
Toffanin ChiaraUniversity of PaviaDirección de correo verificada de unipv.it
Chiara Dalla ManProfessore Associato, Padova UniversityDirección de correo verificada de dei.unipd.it
De Nicolao GiuseppeProfessore di Automatica, Università di PaviaDirección de correo verificada de unipv.it
Claudio CobelliFull Professor of Bioengineering, University of PadovaDirección de correo verificada de dei.unipd.it
M.G. PottersDirección de correo verificada de tudelft.nl
Marco GallieriSenior Data Scientist, DXT Commodities. Former F1 Engineer, Cambridge PhD.Dirección de correo verificada de nnaisense.com
Andrzej StankiewiczTU Delft, DSM, Industrial Chemistry Research Institute, Warsaw University of TechnologyDirección de correo verificada de tudelft.nl
Aneri MuniETH ZurichDirección de correo verificada de student.ethz.ch

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Marco Forgione

Dalle Molle Institute for ArtificiaI Intelligence, SUPSI-USI, Lugano

Dirección de correo verificada de idsia.ch - Página principal

System Identification Model Predictive Control Machine Learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	147	2019
Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial L Magni, M Forgione, C Toffanin, C Dalla Man, B Kovatchev, ... Journal of diabetes science and technology 3 (5), 1091-1098, 2009	123	2009
Continuous-time system identification with neural networks: Model structures and fitting criteria M Forgione, D Piga European Journal of Control 59, 69-81, 2021	84	2021
Robot control parameters auto-tuning in trajectory tracking applications L Roveda, M Forgione, D Piga Control Engineering Practice 101, 104488, 2020	52	2020
dynoNet: A neural network architecture for learning dynamical systems M Forgione, D Piga International Journal of Adaptive Control and Signal Processing 35 (4), 612-626, 2021	50	2021
Efficient calibration of embedded MPC M Forgione, D Piga, A Bemporad IFAC-PapersOnLine 53 (2), 5189-5194, 2020	45	2020
Data-driven model improvement for model-based control M Forgione, X Bombois, PMJ Van den Hof Automatica 52, 118-124, 2015	43	2015
Model structures and fitting criteria for system identification with neural networks M Forgione, D Piga 2020 IEEE 14th International Conference on Application of Information and …, 2020	37	2020
Experiment design for parameter estimation in nonlinear systems based on multilevel excitation M Forgione, X Bombois, PMJ Van den Hof, H Hjalmarsson 2014 European Control Conference (ECC), 25-30, 2014	31	2014
Rapid crystallization process development strategy from lab to industrial scale with PAT tools in skid configuration SS Kadam, JAW Vissers, M Forgione, RM Geertman, PJ Daudey, ... Organic Process Research & Development 16 (5), 769-780, 2012	26	2012
Integrated neural networks for nonlinear continuous-time system identification B Mavkov, M Forgione, D Piga IEEE Control Systems Letters 4 (4), 851-856, 2020	21	2020
Optimal experiment design in closed loop with unknown, nonlinear and implicit controllers using stealth identification MG Potters, X Bombois, M Forgione, PE Modén, M Lundh, H Hjalmarsson, ... 2014 European Control Conference (ECC), 726-731, 2014	17	2014
On the adaptation of recurrent neural networks for system identification M Forgione, A Muni, D Piga, M Gallieri Automatica 155, 111092, 2023	16	2023
Least costly closed-loop performance diagnosis and plant re-identification A Mesbah, X Bombois, M Forgione, H Hjalmarsson, PMJV Hof International Journal of Control 88 (11), 2264-2276, 2015	15	2015
Learning neural state-space models: Do we need a state estimator? M Forgione, M Mejari, D Piga arXiv preprint arXiv:2206.12928, 2022	12	2022
Batch-to-batch model improvement for cooling crystallization M Forgione, G Birpoutsoukis, X Bombois, A Mesbah, PJ Daudey, ... Control Engineering Practice 41, 72-82, 2015	12	2015
Iterative learning control of supersaturation in batch cooling crystallization M Forgione, A Mesbah, X Bombois, PMJ Van den Hof 2012 American Control Conference (ACC), 6455-6460, 2012	11	2012
Direct identification of continuous-time LPV state-space models via an integral architecture M Mejari, B Mavkov, M Forgione, D Piga Automatica 142, 110407, 2022	9	2022
Experiment design for batch-to-batch model-based learning control M Forgione, X Bombois, PMJ Van den Hof 2013 American Control Conference, 3912-3917, 2013	8	2013
From system models to class models: An in-context learning paradigm M Forgione, F Pura, D Piga IEEE Control Systems Letters, 2023	7	2023

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