| 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 | 112 | 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 | 55 | 2019 |
| Data-driven model improvement for model-based control M Forgione, X Bombois, PMJ Van den Hof Automatica 52, 118-124, 2015 | 35 | 2015 |
| 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 | 28 | 2014 |
| Robot control parameters auto-tuning in trajectory tracking applications L Roveda, M Forgione, D Piga Control Engineering Practice 101, 104488, 2020 | 23 | 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 | 23 | 2012 |
| 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 |
| Efficient calibration of embedded MPC M Forgione, D Piga, A Bemporad IFAC-PapersOnLine 53 (2), 5189-5194, 2020 | 15 | 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 | 14 | 2020 |
| 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 | 14 | 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 | 12 | 2012 |
| Continuous-time system identification with neural networks: model structures and fitting criteria M Forgione, D Piga European Journal of Control 59, 69-81, 2021 | 11 | 2021 |
| 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 | 10 | 2021 |
| 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 | 10 | 2015 |
| Integrated neural networks for nonlinear continuous-time system identification B Mavkov, M Forgione, D Piga IEEE Control Systems Letters 4 (4), 851-856, 2020 | 9 | 2020 |
| 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 | 9 | 2013 |
| 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 | 7 | 2012 |
| 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 | 6 | 2012 |
| Least-costly experiment design for uni-parametric linear models: An analytical approach MG Potters, M Forgione, X Bombois, PMJ Van den Hof 2015 European Control Conference (ECC), 848-853, 2015 | 4 | 2015 |
| Two-stage robot controller auto-tuning methodology for trajectory tracking applications L Roveda, M Forgione, D Piga IFAC-PapersOnLine 53 (2), 8724-8731, 2020 | 2 | 2020 |
Paul M.J. Van den HofProfessor Eindhoven University of TechnologyVerified email at tue.nl
