| Constraint-tightening and stability in stochastic model predictive control M Lorenzen, F Dabbene, R Tempo, F Allgöwer IEEE Transactions on Automatic Control, 2017 | 224 | 2017 |
| Robust MPC with recursive model update M Lorenzen, M Cannon, F Allgöwer Automatica 103, 461-471, 2019 | 202 | 2019 |
| Adaptive model predictive control with robust constraint satisfaction M Lorenzen, F Allgöwer, M Cannon IFAC-PapersOnLine 50 (1), 3313-3318, 2017 | 77 | 2017 |
| Stochastic MPC with offline uncertainty sampling M Lorenzen, F Dabbene, R Tempo, F Allgöwer Automatica 81, 176-183, 2017 | 77 | 2017 |
| An offline-sampling SMPC framework with application to autonomous space maneuvers M Mammarella, M Lorenzen, E Capello, H Park, F Dabbene, G Guglieri, ... IEEE Transactions on Control Systems Technology 28 (2), 388-402, 2018 | 47 | 2018 |
| Robust economic model predictive control using stochastic information FA Bayer, M Lorenzen, MA Müller, F Allgöwer Automatica 74, 151-161, 2016 | 47 | 2016 |
| An improved constraint-tightening approach for stochastic MPC M Lorenzen, F Allgöwer, F Dabbene, R Tempo American Control Conference (ACC), 2015, 944-949, 2015 | 31 | 2015 |
| Stochastic model predictive control without terminal constraints M Lorenzen, MA Müller, F Allgöwer International Journal of Robust and Nonlinear Control, 2017 | 24 | 2017 |
| Chance-constrained sets approximation: A probabilistic scaling approach M Mammarella, V Mirasierra, M Lorenzen, T Alamo, F Dabbene Automatica 137, 110108, 2022 | 20 | 2022 |
| Distributed local stabilization in formation control M Lorenzen, MA Belabbas 2014 European Control Conference (ECC), 2914-2919, 2014 | 18 | 2014 |
| Scenario-based stochastic MPC with guaranteed recursive feasibility M Lorenzen, F Allgöwer, F Dabbene, R Tempo 2015 54th IEEE conference on decision and control (CDC), 4958-4963, 2015 | 17 | 2015 |
| Safe approximations of chance constrained sets by probabilistic scaling T Alamo, V Mirasierra, F Dabbene, M Lorenzen 2019 18th European Control Conference (ECC), 1380-1385, 2019 | 13 | 2019 |
| Computationally efficient stochastic MPC: A probabilistic scaling approach M Mammarella, T Alamo, F Dabbene, M Lorenzen 2020 IEEE Conference on Control Technology and Applications (CCTA), 25-30, 2020 | 12 | 2020 |
| A general sampling-based SMPC approach to spacecraft proximity operations M Mammarella, E Capello, M Lorenzen, F Dabbene, F Allgower 2017 IEEE 56th annual conference on decision and control (CDC), 4521-4526, 2017 | 11 | 2017 |
| Facilitating learning progress in a first control course via Matlab apps A Koch, M Lorenzen, P Pauli, F Allgöwer IFAC-PapersOnLine 53 (2), 17356-17361, 2020 | 8* | 2020 |
| A distributed solution to the adjustable robust economic dispatch problem M Lorenzen, M Bürger, G Notarstefano, F Allgöwer IFAC Proceedings Volumes 46 (27), 75-80, 2013 | 7 | 2013 |
| Improving performance in robust economic MPC using stochastic information FA Bayer, M Lorenzen, MA Müller, F Allgöwer IFAC-PapersOnLine 48 (23), 410-415, 2015 | 6 | 2015 |
| Stabilizing stochastic MPC without terminal constraints M Lorenzen, MA Müller, F Allgöwer 2017 American Control Conference (ACC), 5636-5641, 2017 | 5 | 2017 |
| Chance constrained sets approximation: A probabilistic scaling approach--EXTENDED VERSION M Mammarella, V Mirasierra, M Lorenzen, T Alamo, F Dabbene arXiv preprint arXiv:2101.06052, 2021 | 4 | 2021 |
| Predictive control under uncertainty: from conceptual aspects to computational approaches M Lorenzen Logos Verlag Berlin GmbH, 2019 | | 2019 |
Frank AllgöwerUniversity of Stuttgart, Universität StuttgartBestätigte E-Mail-Adresse bei ist.uni-stuttgart.de
