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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
5812020
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems
KP Wabersich, MN Zeilinger
Automatica 129, 109597, 2021
190*2021
Linear model predictive safety certification for learning-based control
KP Wabersich, MN Zeilinger
2018 IEEE Conference on Decision and Control (CDC), 7130-7135, 2018
1672018
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
942021
Recursively feasible stochastic model predictive control using indirect feedback
L Hewing, KP Wabersich, MN Zeilinger
Automatica 119, 109095, 2020
722020
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
412022
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
392021
Wiggling through complex traffic: Planning trajectories constrained by predictions
J Schlechtriemen, KP Wabersich, KD Kuhnert
2016 IEEE Intelligent Vehicles Symposium (IV), 1293-1300, 2016
382016
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
372018
Predictive control barrier functions: Enhanced safety mechanisms for learning-based control
KP Wabersich, MN Zeilinger
IEEE Transactions on Automatic Control, 2022
332022
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
302018
Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems
KP Wabersich, AJ Taylor, JJ Choi, K Sreenath, CJ Tomlin, AD Ames, ...
IEEE Control Systems Magazine 43 (5), 137-177, 2023
282023
Distributed model predictive safety certification for learning-based control
S Muntwiler, KP Wabersich, A Carron, MN Zeilinger
IFAC-PapersOnLine 53 (2), 5258-5265, 2020
262020
Cautious Bayesian MPC: Regret analysis and bounds on the number of unsafe learning episodes
KP Wabersich, MN Zeilinger
IEEE Transactions on Automatic Control, 2022
20*2022
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
192020
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
142022
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
13*2021
Nonlinear learning‐based model predictive control supporting state and input dependent model uncertainty estimates
KP Wabersich, MN Zeilinger
international journal of robust and nonlinear control 31 (18), 8897-8915, 2021
122021
State space models vs. multi-step predictors in predictive control: Are state space models complicating safe data-driven designs?
J Köhler, KP Wabersich, J Berberich, MN Zeilinger
2022 IEEE 61st Conference on Decision and Control (CDC), 491-498, 2022
102022
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
102021
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