Marco Forgione
Marco Forgione
Dalle Molle Institute for ArtificiaI Intelligence, SUPSI-USI, Lugano
Verified email at - Homepage
Cited by
Cited by
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
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
Continuous-time system identification with neural networks: Model structures and fitting criteria
M Forgione, D Piga
European Journal of Control 59, 69-81, 2021
Robot control parameters auto-tuning in trajectory tracking applications
L Roveda, M Forgione, D Piga
Control Engineering Practice 101, 104488, 2020
Data-driven model improvement for model-based control
M Forgione, X Bombois, PMJ Van den Hof
Automatica 52, 118-124, 2015
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
Efficient calibration of embedded MPC
M Forgione, D Piga, A Bemporad
IFAC-PapersOnLine 53 (2), 5189-5194, 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
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
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
Integrated neural networks for nonlinear continuous-time system identification
B Mavkov, M Forgione, D Piga
IEEE Control Systems Letters 4 (4), 851-856, 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
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
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
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
On the adaptation of recurrent neural networks for system identification
M Forgione, A Muni, D Piga, M Gallieri
Automatica 155, 111092, 2023
Learning neural state-space models: do we need a state estimator?
M Forgione, M Mejari, D Piga
arXiv preprint arXiv:2206.12928, 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
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
Two-stage robot controller auto-tuning methodology for trajectory tracking applications
L Roveda, M Forgione, D Piga
IFAC-PapersOnLine 53 (2), 8724-8731, 2020
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