571 citations · 845 across the 12 of their papers we have counts for
12 papers
Sequential Convex Programming for Optimal Line of Sight Steering in Agile Missions
Jesús Ramírez, Lukas Hewing
The trend toward onboard autonomy and spacecraft minimization present significant potential for advances in efficient Line of Sight management by making optimal use of the limited…
Contextual Tuning of Model Predictive Control for Autonomous Racing
Lukas P. Fröhlich, Christian Küttel, Elena Arcari +3
Learning-based model predictive control has been widely applied in autonomous racing to improve the closed-loop behaviour of vehicles in a data-driven manner. When environmental co…
Volume Control of Low-Cost Ventilator with Automatic Set-Point Adaptation
Lukas Hewing, Marcel Menner, Nikolaos Tachatos +6
This paper considers the control design for a low-cost ventilator that is based on a manual resuscitator bag (also known as AmbuBag) to pump air into the lungs of a patient who is…
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Simon Muntwiler, Kim P. Wabersich, Lukas Hewing +1
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust fo…
Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty
Elena Arcari, Lukas Hewing, Max Schlichting +1
Designing controllers for systems affected by model uncertainty can prove to be a challenge, especially when seeking the optimal compromise between the conflicting goals of identif…
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Lukas Hewing, Elena Arcari, Lukas P. Fröhlich +1
Established techniques for simulation and prediction with Gaussian process (GP) dynamics often implicitly make use of an independence assumption on successive function evaluations…
An Approximate Dynamic Programming Approach for Dual Stochastic Model Predictive Control
Elena Arcari, Lukas Hewing, Melanie N. Zeilinger
Dual control explicitly addresses the problem of trading off active exploration and exploitation in the optimal control of partially unknown systems. While the problem can be cast…
Probabilistic model predictive safety certification for learning-based control
Kim P. Wabersich, Lukas Hewing, Andrea Carron +1
Reinforcement learning (RL) methods have demonstrated their efficiency in simulation environments. However, many applications for which RL offers great potential, such as autonomou…
Recursively Feasible Stochastic Model Predictive Control using Indirect Feedback
Lukas Hewing, Kim P. Wabersich, Melanie N. Zeilinger
We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequence…
Stochastic Model Predictive Control for Linear Systems using Probabilistic Reachable Sets
Lukas Hewing, Melanie N. Zeilinger
In this paper we propose a stochastic model predictive control (MPC) algorithm for linear discrete-time systems affected by possibly unbounded additive disturbances and subject to…
Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars
Lukas Hewing, Alexander Liniger, Melanie N. Zeilinger
This paper presents an adaptive high performance control method for autonomous miniature race cars. Racing dynamics are notoriously hard to model from first principles, which is ad…
Cautious Model Predictive Control using Gaussian Process Regression
Lukas Hewing, Juraj Kabzan, Melanie N. Zeilinger
Gaussian process (GP) regression has been widely used in supervised machine learning due to its flexibility and inherent ability to describe uncertainty in function estimation. In…