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Marco Forgione
Marco Forgione
Dalle Molle Institute for ArtificiaI Intelligence, SUPSI-USI, Lugano
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Titel
Zitiert von
Zitiert von
Jahr
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
1612019
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
1222009
Continuous-time system identification with neural networks: Model structures and fitting criteria
M Forgione, D Piga
European Journal of Control 59, 69-81, 2021
852021
Robot control parameters auto-tuning in trajectory tracking applications
L Roveda, M Forgione, D Piga
Control Engineering Practice 101, 104488, 2020
572020
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
552021
Data-driven model improvement for model-based control
M Forgione, X Bombois, PMJ Van den Hof
Automatica 52, 118-124, 2015
452015
Efficient calibration of embedded MPC
M Forgione, D Piga, A Bemporad
IFAC-PapersOnLine 53 (2), 5189-5194, 2020
442020
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
392020
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
312014
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
262012
Integrated neural networks for nonlinear continuous-time system identification
B Mavkov, M Forgione, D Piga
IEEE Control Systems Letters 4 (4), 851-856, 2020
232020
On the adaptation of recurrent neural networks for system identification
M Forgione, A Muni, D Piga, M Gallieri
Automatica 155, 111092, 2023
172023
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
172015
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
172014
Learning neural state-space models: do we need a state estimator?
M Forgione, M Mejari, D Piga
arXiv preprint arXiv:2206.12928, 2022
152022
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
132015
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
112023
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
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
102022
Two-stage robot controller auto-tuning methodology for trajectory tracking applications
L Roveda, M Forgione, D Piga
IFAC-PapersOnLine 53 (2), 8724-8731, 2020
82020
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