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Marco Forgione
Marco Forgione
Dalle Molle Institute for ArtificiaI Intelligence, SUPSI-USI, Lugano
Verified email at idsia.ch - Homepage
Title
Cited by
Cited by
Year
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
1172009
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
1062019
Continuous-time system identification with neural networks: Model structures and fitting criteria
M Forgione, D Piga
European Journal of Control 59, 69-81, 2021
432021
Robot control parameters auto-tuning in trajectory tracking applications
L Roveda, M Forgione, D Piga
Control Engineering Practice 101, 104488, 2020
402020
Data-driven model improvement for model-based control
M Forgione, X Bombois, PMJ Van den Hof
Automatica 52, 118-124, 2015
362015
Efficient calibration of embedded MPC
M Forgione, D Piga, A Bemporad
IFAC-PapersOnLine 53 (2), 5189-5194, 2020
312020
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
292014
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
242020
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
232012
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
212021
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
162014
Integrated neural networks for nonlinear continuous-time system identification
B Mavkov, M Forgione, D Piga
IEEE Control Systems Letters 4 (4), 851-856, 2020
152020
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
152015
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
112015
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
112012
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
82013
Learning neural state-space models: do we need a state estimator?
M Forgione, M Mejari, D Piga
arXiv preprint arXiv:2206.12928, 2022
62022
A unified experiment design framework for detection and identification in closed-loop performance diagnosis
A Mesbah, X Bombois, M Forgione, JHA Ludlage, PE Modén, ...
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2152-2157, 2012
62012
Batch-to-batch strategies for cooling crystallization
M Forgione, A Mesbah, X Bombois, PMJ Van den Hof
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 6364-6369, 2012
62012
On the adaptation of recurrent neural networks for system identification
M Forgione, A Muni, D Piga, M Gallieri
Automatica 155, 111092, 2023
42023
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Articles 1–20