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Matthias Lorenzen
Matthias Lorenzen
Verified email at ist.uni-stuttgart.de
Title
Cited by
Cited by
Year
Constraint-tightening and stability in stochastic model predictive control
M Lorenzen, F Dabbene, R Tempo, F Allgöwer
IEEE Transactions on Automatic Control, 2016
1362016
Robust MPC with recursive model update
M Lorenzen, M Cannon, F Allgöwer
Automatica 103, 461-471, 2019
962019
Adaptive model predictive control with robust constraint satisfaction
M Lorenzen, F Allgöwer, M Cannon
IFAC-PapersOnLine 50 (1), 3313-3318, 2017
562017
Stochastic MPC with offline uncertainty sampling
M Lorenzen, F Dabbene, R Tempo, F Allgöwer
Automatica 81, 176-183, 2017
522017
Robust economic model predictive control using stochastic information
FA Bayer, M Lorenzen, MA Müller, F Allgöwer
Automatica 74, 151-161, 2016
362016
An improved constraint-tightening approach for stochastic MPC
M Lorenzen, F Allgöwer, F Dabbene, R Tempo
American Control Conference (ACC), 2015, 944-949, 2015
282015
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
242018
Distributed local stabilization in formation control
M Lorenzen, MA Belabbas
2014 European Control Conference (ECC), 2914-2919, 2014
182014
Stochastic model predictive control without terminal constraints
M Lorenzen, MA Müller, F Allgöwer
International Journal of Robust and Nonlinear Control, 2017
132017
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
132015
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
92019
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
82020
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
82017
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
72013
Improving performance in robust economic MPC using stochastic information
FA Bayer, M Lorenzen, MA Müller, F Allgöwer
IFAC-PapersOnLine 48 (23), 410-415, 2015
62015
Stabilizing stochastic MPC without terminal constraints
M Lorenzen, MA Müller, F Allgöwer
2017 American Control Conference (ACC), 5636-5641, 2017
52017
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
42021
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
22020
Chance-constrained sets approximation: A probabilistic scaling approach
M Mammarella, V Mirasierra, M Lorenzen, T Alamo, F Dabbene
Automatica 137, 110108, 2022
12022
Facilitating learning progress in a first control course via Matlab Apps
A Romer, M Lorenzen, P Pauli, F Allgöwer
Proc. 21st IFAC World Congress. Submitted- preprint online https://www. ist …, 2019
12019
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