Automatic testing and minimax optimization of system parameters for best worst-case performance. - Kim Peter Wabersich; Marc Toussaint (IROS, 2015)

Wiggling through complex traffic: Planning trajectories constrained by predictions. - Julian Schlechtriemen; Kim Peter Wabersich; Klaus-Dieter Kuhnert (Intelligent Vehicles Symposium, 2016)

Advancing Bayesian Optimization: The Mixed-Global-Local (MGL) Kernel and Length-Scale Cool Down. - Kim Peter Wabersich; Marc Toussaint (CoRR, 2016)

Linear Model Predictive Safety Certification for Learning-Based Control. - Kim Peter Wabersich; Melanie N. Zeilinger (CDC, 2018)

On a Correspondence between Probabilistic and Robust Invariant Sets for Linear Systems. - Lukas Hewing; Andrea Carron; Kim Peter Wabersich; Melanie N. Zeilinger (ECC, 2018)

Economic Model Predictive Control for Robust Periodic Operation with Guaranteed Closed-Loop Performance. - Kim Peter Wabersich; Florian A. Bayer; Matthias Albrecht Müller; Frank Allgöwer (ECC, 2018)

Scalable synthesis of safety certificates from data with application to learning-based control. - Kim Peter Wabersich; Melanie N. Zeilinger (ECC, 2018)

Safe exploration of nonlinear dynamical systems: A predictive safety filter for reinforcement learning. - Kim Peter Wabersich; Melanie N. Zeilinger (CoRR, 2018)

Distributed Model Predictive Safety Certification for Learning-based Control. - Simon Muntwiler; Kim Peter Wabersich; Andrea Carron; Melanie N. Zeilinger (CoRR, 2019)

Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling. - Kim Peter Wabersich; Melanie N. Zeilinger (L4DC, 2020)

Learning-Based Model Predictive Control: Toward Safe Learning in Control. - Lukas Hewing; Kim Peter Wabersich; Marcel Menner; Melanie N. Zeilinger (Annu. Rev. Control. Robotics Auton. Syst., 2020)

Recursively feasible stochastic model predictive control using indirect feedback. - Lukas Hewing; Kim Peter Wabersich; Melanie N. Zeilinger (Autom., 2020)

Performance and safety of Bayesian model predictive control: Scalable model-based RL with guarantees. - Kim Peter Wabersich; Melanie N. Zeilinger (CoRR, 2020)

Adaptive Model Predictive Safety Certification for Learning-based Control. - Alexandre Didier; Kim Peter Wabersich; Melanie N. Zeilinger (CDC, 2021)

Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction. - Simon Muntwiler; Kim Peter Wabersich; Lukas Hewing; Melanie N. Zeilinger (ECC, 2021)

A predictive safety filter for learning-based control of constrained nonlinear dynamical systems. - Kim Peter Wabersich; Melanie N. Zeilinger (Autom., 2021)

Adaptive Model Predictive Safety Certification for Learning-based Control - Extended Version. - Alexandre Didier; Kim Peter Wabersich; Melanie N. Zeilinger (CoRR, 2021)

A Predictive Safety Filter for Learning-Based Racing Control. - Ben Tearle; Kim Peter Wabersich; Andrea Carron; Melanie N. Zeilinger (IEEE Robotics Autom. Lett., 2021)

Plug-and-Play Distributed Safety Verification for Linear Control Systems With Bounded Uncertainties. - Andrea Carron; Kim Peter Wabersich; Melanie N. Zeilinger (IEEE Trans. Control. Netw. Syst., 2021)

Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon? - Ali Mesbah 002; Kim Peter Wabersich; Angela P. Schoellig; Melanie N. Zeilinger; Sergio Lucia; Thomas A. Badgwell; Joel A. Paulson (ACC, 2022)

State space models vs. multi-step predictors in predictive control: Are state space models complicating safe data-driven designs? - Johannes Köhler 001; Kim Peter Wabersich; Julian Berberich; Melanie N. Zeilinger (CDC, 2022)

Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers. - Simon Muntwiler; Kim Peter Wabersich; Melanie N. Zeilinger (L4DC, 2022)

A Soft Constrained MPC Formulation Enabling Learning From Trajectories With Constraint Violations. - Kim Peter Wabersich; Raamadaas Krishnadas; Melanie N. Zeilinger (IEEE Control. Syst. Lett., 2022)

Probabilistic Model Predictive Safety Certification for Learning-Based Control. - Kim Peter Wabersich; Lukas Hewing; Andrea Carron; Melanie N. Zeilinger (IEEE Trans. Autom. Control., 2022)

Approximate Predictive Control Barrier Functions using Neural Networks: A Computationally Cheap and Permissive Safety Filter. - Alexandre Didier; Robin C. Jacobs; Jerome Sieber; Kim Peter Wabersich; Melanie N. Zeilinger (ECC, 2023)

LQG for Constrained Linear Systems: Indirect Feedback Stochastic MPC with Kalman Filtering. - Simon Muntwiler; Kim Peter Wabersich; Robert Miklos; Melanie N. Zeilinger (ECC, 2023)

Predictive Control Barrier Functions: Enhanced Safety Mechanisms for Learning-Based Control. - Kim Peter Wabersich; Melanie N. Zeilinger (IEEE Trans. Autom. Control., 2023)

Cautious Bayesian MPC: Regret Analysis and Bounds on the Number of Unsafe Learning Episodes. - Kim Peter Wabersich; Melanie N. Zeilinger (IEEE Trans. Autom. Control., 2023)

Stability Mechanisms for Predictive Safety Filters. - Elias Milios; Kim Peter Wabersich; Felix Berkel; Lukas Schwenkel (CDC, 2024)

Learning soft constrained MPC value functions: Efficient MPC design and implementation providing stability and safety guarantees. - Nicolas Chatzikiriakos; Kim Peter Wabersich; Felix Berkel; Patricia Pauli; Andrea Iannelli (L4DC, 2024)

Predictive stability filters for nonlinear dynamical systems affected by disturbances. - Alexandre Didier; Andrea Zanelli; Kim Peter Wabersich; Melanie N. Zeilinger (CoRR, 2024)

L4acados: Learning-based models for acados, applied to Gaussian process-based predictive control. - Amon Lahr; Joshua Näf; Kim Peter Wabersich; Jonathan Frey; Pascal Siehl; Andrea Carron; Moritz Diehl; Melanie N. Zeilinger (CoRR, 2024)

Contract-based hierarchical control using predictive feasibility value functions. - Felix Berkel; Kim Peter Wabersich; Hongxi Xiang; Elias Milios (CoRR, 2025)

Set-Based Control Barrier Functions and Safety Filters. - Kim Peter Wabersich; Felix Berkel; Felix Gruber; Sven Reimann (CoRR, 2025)