18 citations · 47 across the 37 of their papers we have counts for
7 papers · 1 filter
Anytime Plug-and-Play Control with Contract-Based Distributed MPC
Sabrina Bodmer, Danilo Saccani, Melanie N. Zeilinger +1
A central challenge in many mobile multi-robot applications is that communication topologies are inherently time-varying. Agents may enter or exit the network and such changes cann…
Guaranteed Robust Nonlinear MPC via Disturbance Feedback
Antoine P. Leeman, Johannes Köhler, Melanie N. Zeilinger
Robots must satisfy safety-critical state and input constraints despite disturbances and model mismatch. We introduce a robust model predictive control (RMPC) formulation that is f…
Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework
Manish Prajapat, Amon Lahr, Johannes Köhler +2
Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-…
Inherently robust suboptimal MPC for autonomous racing with anytime feasible SQP
Logan Numerow, Andrea Zanelli, Andrea Carron +1
In recent years, the increasing need for high-performance controllers in applications like autonomous driving has motivated the development of optimization routines tailored to spe…
Probabilistic ODE Solvers for Integration Error-Aware Numerical Optimal Control
Amon Lahr, Filip Tronarp, Nathanael Bosch +3
Appropriate time discretization is crucial for real-time applications of numerical optimal control, such as nonlinear model predictive control. However, if the discretization error…
Fast System Level Synthesis: Robust Model Predictive Control using Riccati Recursions
Antoine P. Leeman, Johannes Köhler, Florian Messerer +3
System level synthesis enables improved robust MPC formulations by allowing for joint optimization of the nominal trajectory and controller. This paper introduces a tailored algori…