2 citations · 3 across the 14 of their papers we have counts for
5 papers · 1 filter
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-…
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…
Efficient Zero-Order Robust Optimization for Real-Time Model Predictive Control with acados
Jonathan Frey, Yunfan Gao, Florian Messerer +3
Robust and stochastic optimal control problem (OCP) formulations allow a systematic treatment of uncertainty, but are typically associated with a high computational cost. The recen…
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…