Marco Forgione

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Dario PigaSenior Researcher at IDSIA - Dalle Molle Institute for ArtificiaI Intelligence, SUPSI-USI, LuganoVerified email at supsi.ch
Xavier BomboisCNRS Research Director, Laboratoire Ampère, Ecole Centrale de LyonVerified email at ec-lyon.fr
Paul M.J. Van den HofProfessor Eindhoven University of TechnologyVerified email at tue.nl
Loris RovedaAssociate Professor @ PoliMi; Senior Researcher @ IDSIA-SUPSI/USIVerified email at polimi.it
Manas MejariIDSIA Dalle Molle Institute for Artificial Intelligence, SwitzerlandVerified email at idsia.ch
Simone FormentinAssociate Professor, Politecnico di MilanoVerified email at polimi.it
Ali MesbahUniversity of California, BerkeleyVerified email at berkeley.edu
Håkan HjalmarssonCentre for Advanced Bioproduction, Linnaeus Centre ACCESS, School of EE and CSVerified email at kth.se
Alberto BemporadProfessor of Control Systems, IMT Lucca, ItalyVerified email at imtlucca.it
Herman J.M. KramerAssociate professor, Delft University of TechnologyVerified email at tudelft.nl
Bojan MavkovAssociate Professor, Université Côte d'AzurVerified email at i3s.unice.fr
Magni LaloProfessor of Automatic Control at University of PaviaVerified email at unipv.it
Toffanin ChiaraUniversity of PaviaVerified email at unipv.it
Chiara Dalla ManProfessore Associato, Padova UniversityVerified email at dei.unipd.it
De Nicolao GiuseppeProfessore di Automatica, Università di PaviaVerified email at unipv.it
Claudio CobelliFull Professor of Bioengineering, University of PadovaVerified email at dei.unipd.it
Filippo PuraSUPSI, IDSIAVerified email at supsi.ch
M.G. PottersVerified email at tudelft.nl
Marco GallieriSenior Data Scientist, DXT Commodities. Former F1 Engineer, Cambridge PhD.Verified email at nnaisense.com
Andrzej StankiewiczTU Delft, DSM, Industrial Chemistry Research Institute, Warsaw University of TechnologyVerified email at tudelft.nl

Marco Forgione

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

Verified email at idsia.ch - Homepage

System Identification Model Predictive Control Machine Learning


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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	161	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	122	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	85	2021
Robot control parameters auto-tuning in trajectory tracking applications L Roveda, M Forgione, D Piga Control Engineering Practice 101, 104488, 2020	57	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	55	2021
Data-driven model improvement for model-based control M Forgione, X Bombois, PMJ Van den Hof Automatica 52, 118-124, 2015	45	2015
Efficient calibration of embedded MPC M Forgione, D Piga, A Bemporad IFAC-PapersOnLine 53 (2), 5189-5194, 2020	44	2020
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	39	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	23	2020
On the adaptation of recurrent neural networks for system identification M Forgione, A Muni, D Piga, M Gallieri Automatica 155, 111092, 2023	17	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	17	2015
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
Learning neural state-space models: do we need a state estimator? M Forgione, M Mejari, D Piga arXiv preprint arXiv:2206.12928, 2022	15	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	13	2015
From system models to class models: An in-context learning paradigm M Forgione, F Pura, D Piga IEEE Control Systems Letters 7, 3513-3518, 2023	11	2023
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	10	2022
Two-stage robot controller auto-tuning methodology for trajectory tracking applications L Roveda, M Forgione, D Piga IFAC-PapersOnLine 53 (2), 8724-8731, 2020	8	2020

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