2 citations · 3 across the 26 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…
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…
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…
Robust Nonlinear Optimal Control via System Level Synthesis
Antoine P. Leeman, Johannes Köhler, Andrea Zanelli +2
This paper addresses the problem of finite horizon constrained robust optimal control for nonlinear systems subject to norm-bounded disturbances. To this end, the underlying uncert…
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-…
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…