571 citations · 1.3k across the 123 of their papers we have counts for
17 papers · 1 filter
Predictive safety filter using system level synthesis
Antoine P. Leeman, Johannes Köhler, Samir Benanni +1
Safety filters provide modular techniques to augment potentially unsafe control inputs (e.g. from learning-based controllers or humans) with safety guarantees in the form of constr…
LQG for Constrained Linear Systems: Indirect Feedback Stochastic MPC with Kalman Filtering
Simon Muntwiler, Kim P. Wabersich, Robert Miklos +1
We present an output feedback stochastic model predictive control (SMPC) approach for linear systems subject to Gaussian disturbances and measurement noise and probabilistic constr…
Zero-Order Optimization for Gaussian Process-based Model Predictive Control
Amon Lahr, Andrea Zanelli, Andrea Carron +1
By enabling constraint-aware online model adaptation, model predictive control using Gaussian process (GP) regression has exhibited impressive performance in real-world application…
Approximate Predictive Control Barrier Functions using Neural Networks: A Computationally Cheap and Permissive Safety Filter
Alexandre Didier, Robin C. Jacobs, Jerome Sieber +2
A predictive control barrier function (PCBF) based safety filter is a modular framework to verify safety of a control input by predicting a future trajectory. The approach relies o…
Asynchronous Computation of Tube-based Model Predictive Control
Jerome Sieber, Andrea Zanelli, Antoine P. Leeman +2
Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. While techniques that…
Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation
Elena Arcari, Maria Vittoria Minniti, Anna Scampicchio +4
Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-prin…
Generalised Regret Optimal Controller Synthesis for Constrained Systems
Alexandre Didier, Melanie N. Zeilinger
This paper presents a synthesis method for the generalised dynamic regret problem, comparing the performance of a strictly causal controller to the optimal non-causal controller un…
Near-Optimal Multi-Agent Learning for Safe Coverage Control
Manish Prajapat, Matteo Turchetta, Melanie N. Zeilinger +1
In multi-agent coverage control problems, agents navigate their environment to reach locations that maximize the coverage of some density. In practice, the density is rarely known…
Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control
Andrea Carron, Sabrina Bodmer, Lukas Vogel +6
From both an educational and research point of view, experiments on hardware are a key aspect of robotics and control. In the last decade, many open-source hardware and software fr…
Robust adaptive MPC using control contraction metrics
András Sasfi, Melanie N. Zeilinger, Johannes Köhler
We present a robust adaptive model predictive control (MPC) framework for nonlinear continuous-time systems with bounded parametric uncertainty and additive disturbance. We utilize…
A model predictive control framework with robust stability guarantees under unbounded disturbances
Johannes Köhler, Melanie N. Zeilinger
To address feasibility issues in model predictive control (MPC), most implementations relax state constraints by using slack variables and adding a penalty to the cost. We propose…
Stochastic MPC with robustness to bounded parametric uncertainty
Elena Arcari, Andrea Iannelli, Andrea Carron +1
The performance of model-based control techniques strongly depends on the quality of the employed dynamics model. If strong guarantees are desired, it is therefore common to robust…
A Feasible Sequential Linear Programming Algorithm with Application to Time-Optimal Path Planning Problems
David Kiessling, Andrea Zanelli, Armin Nurkanović +5
In this paper, we propose a Feasible Sequential Linear Programming (FSLP) algorithm applied to time-optimal control problems (TOCP) obtained through direct multiple shooting discre…
State space models vs. multi-step predictors in predictive control: Are state space models complicating safe data-driven designs?
Johannes Köhler, Kim P. Wabersich, Julian Berberich +1
This paper contrasts recursive state space models and direct multi-step predictors for linear predictive control. We provide a tutorial exposition for both model structures to solv…
Recursively feasible stochastic predictive control using an interpolating initial state constraint -- extended version
Johannes Köhler, Melanie N. Zeilinger
We present a stochastic model predictive control (SMPC) framework for linear systems subject to possibly unbounded disturbances. State of the art SMPC approaches with closed-loop c…
A System Level Approach to Regret Optimal Control
Alexandre Didier, Jerome Sieber, Melanie N. Zeilinger
We present an optimisation-based method for synthesising a dynamic regret optimal controller for linear systems with potentially adversarial disturbances and known or adversarial i…
A Lyapunov function for robust stability of moving horizon estimation
Julian D. Schiller, Simon Muntwiler, Johannes Köhler +2
We provide a novel robust stability analysis for moving horizon estimation (MHE) using a Lyapunov function. Additionally, we introduce linear matrix inequalities (LMIs) to verify t…