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Kim Peter Wabersich
Kim Peter Wabersich
Bosch Research, Former:ETH Zurich
Bestätigte E-Mail-Adresse bei kimpeter.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Learning-based model predictive control: Toward safe learning in control
L Hewing, KP Wabersich, M Menner, MN Zeilinger
Annual Review of Control, Robotics, and Autonomous Systems 3, 269-296, 2020
3612020
Linear model predictive safety certification for learning-based control
KP Wabersich, MN Zeilinger
2018 IEEE Conference on Decision and Control (CDC), 7130-7135, 2018
1182018
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems
KP Wabersich, MN Zeilinger
Automatica 129, 109597, 2021
107*2021
Probabilistic model predictive safety certification for learning-based control
KP Wabersich, L Hewing, A Carron, MN Zeilinger
IEEE Transactions on Automatic Control 67 (1), 176-188, 2021
592021
Recursively feasible stochastic model predictive control using indirect feedback
L Hewing, KP Wabersich, MN Zeilinger
Automatica 119, 109095, 2020
502020
Scalable synthesis of safety certificates from data with application to learning-based control
KP Wabersich, MN Zeilinger
2018 European Control Conference (ECC), 1691-1697, 2018
352018
Wiggling through complex traffic: Planning trajectories constrained by predictions
J Schlechtriemen, KP Wabersich, KD Kuhnert
2016 IEEE Intelligent Vehicles Symposium (IV), 1293-1300, 2016
332016
On a correspondence between probabilistic and robust invariant sets for linear systems
L Hewing, A Carron, KP Wabersich, MN Zeilinger
2018 European Control Conference (ECC), 1642-1647, 2018
232018
Distributed model predictive safety certification for learning-based control
S Muntwiler, KP Wabersich, A Carron, MN Zeilinger
IFAC-PapersOnLine 53 (2), 5258-5265, 2020
162020
Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling
KP Wabersich, M Zeilinger
Learning for Dynamics and Control, 455-464, 2020
152020
A predictive safety filter for learning-based racing control
B Tearle, KP Wabersich, A Carron, MN Zeilinger
IEEE Robotics and Automation Letters 6 (4), 7635-7642, 2021
132021
Cautious Bayesian MPC: Regret Analysis and Bounds on the Number of Unsafe Learning Episodes
KP Wabersich, MN Zeilinger
IEEE Transactions on Automatic Control, 2022
11*2022
Data-driven distributed stochastic model predictive control with closed-loop chance constraint satisfaction
S Muntwiler, KP Wabersich, L Hewing, MN Zeilinger
2021 European Control Conference (ECC), 210-215, 2021
11*2021
Predictive control barrier functions: Enhanced safety mechanisms for learning-based control
KP Wabersich, MN Zeilinger
IEEE Transactions on Automatic Control, 2022
72022
Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon?
A Mesbah, KP Wabersich, AP Schoellig, MN Zeilinger, S Lucia, ...
2022 American Control Conference (ACC), 342-357, 2022
62022
Learning-based moving horizon estimation through differentiable convex optimization layers
S Muntwiler, KP Wabersich, MN Zeilinger
Learning for Dynamics and Control Conference, 153-165, 2022
62022
A soft constrained MPC formulation enabling learning from trajectories with constraint violations
KP Wabersich, R Krishnadas, MN Zeilinger
IEEE Control Systems Letters 6, 980-985, 2021
62021
Economic model predictive control for robust periodic operation with guaranteed closed-loop performance
KP Wabersich, FA Bayer, MA Müller, F Allgüwer
2018 European Control Conference (ECC), 507-513, 2018
62018
Advancing Bayesian optimization: The mixed-global-local (MGL) kernel and length-scale cool down
KP Wabersich, M Toussaint
arXiv preprint arXiv:1612.03117, 2016
62016
Automatic testing and minimax optimization of system parameters for best worst-case performance
KP Wabersich, M Toussaint
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015
62015
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