Kim Peter Wabersich
Kim Peter Wabersich
PhD Student, ETH Zurich
Verified email at kimpeter.de - Homepage
Title
Cited by
Cited by
Year
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
1182020
Linear model predictive safety certification for learning-based control
KP Wabersich, MN Zeilinger
2018 IEEE Conference on Decision and Control (CDC), 7130-7135, 2018
682018
Safe exploration of nonlinear dynamical systems: A predictive safety filter for reinforcement learning
KP Wabersich, MN Zeilinger
arXiv preprint arXiv:1812.05506, 2018
352018
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
272018
Wiggling through complex traffic: Planning trajectories constrained by predictions
J Schlechtriemen, KP Wabersich, KD Kuhnert
2016 IEEE Intelligent Vehicles Symposium (IV), 1293-1300, 2016
262016
Probabilistic model predictive safety certification for learning-based control
KP Wabersich, L Hewing, A Carron, MN Zeilinger
IEEE Transactions on Automatic Control, 2021
252021
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
192018
Recursively feasible stochastic model predictive control using indirect feedback
L Hewing, KP Wabersich, MN Zeilinger
Automatica 119, 109095, 2020
152020
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems
KP Wabersich, MN Zeilinger
Automatica 129, 109597, 2021
122021
Distributed model predictive safety certification for learning-based control
S Muntwiler, KP Wabersich, A Carron, MN Zeilinger
IFAC-PapersOnLine 53 (2), 5258-5265, 2020
102020
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
92020
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
Advancing Bayesian optimization: The mixed-global-local (MGL) kernel and length-scale cool down
KP Wabersich, M Toussaint
arXiv preprint arXiv:1612.03117, 2016
52016
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
42018
Performance and safety of Bayesian model predictive control: Scalable model-based RL with guarantees
KP Wabersich, MN Zeilinger
arXiv preprint arXiv:2006.03483, 2020
32020
Robust economic model predictive control for periodic operation
KP Wabersich
master thesis, University of Stuttgart, 03 2017.[Online]. Available: www …, 2017
12017
Adaptive Model Predictive Safety Certification for Learning-based Control--Extended Version
A Didier, KP Wabersich, MN Zeilinger
arXiv preprint arXiv:2109.13033, 2021
2021
Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers
S Muntwiler, KP Wabersich, MN Zeilinger
arXiv preprint arXiv:2109.03962, 2021
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, 2021
2021
A soft constrained MPC formulation enabling learning from trajectories with constraint violations
KP Wabersich, R Krishnadas, MN Zeilinger
IEEE Control Systems Letters, 2021
2021
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Articles 1–20