2 citations · 3 across the 14 of their papers we have counts for
7 papers · 1 filter
Multi-Timescale Model Predictive Control for Slow-Fast Systems
Lukas Schroth, Daniel Morton, Amon Lahr +3
Model Predictive Control (MPC) has established itself as the primary methodology for constrained control, enabling autonomy across diverse applications. While model fidelity is cru…
Unifying Sequential Quadratic Programming and Linear-Parameter-Varying Algorithms for Real-Time Model Predictive Control
Kristóf Floch, Amon Lahr, Roland Tóth +1
This paper presents a unified framework that connects sequential quadratic programming (SQP) and the iterative linear-parameter-varying model predictive control (LPV-MPC) technique…
Inverse Optimal Control with Constraint Relaxation
Rahel Rickenbach, Amon Lahr, Melanie N. Zeilinger
Inverse optimal control (IOC) is a promising paradigm for learning and mimicking optimal control strategies from capable demonstrators, or gaining a deeper understanding of their i…
A robust and adaptive MPC formulation for Gaussian process models
Mathieu Dubied, Amon Lahr, Melanie N. Zeilinger +1
In this paper, we present a robust and adaptive model predictive control (MPC) framework for uncertain nonlinear systems affected by bounded disturbances and unmodeled nonlineariti…
Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
Manish Prajapat, Johannes Köhler, Amon Lahr +2
Gaussian Process (GP) regression is shown to be effective for learning unknown dynamics, enabling efficient and safety-aware control strategies across diverse applications. However…
Optimal kernel regression bounds under energy-bounded noise
Amon Lahr, Johannes Köhler, Anna Scampicchio +1
Non-conservative uncertainty bounds are key for both assessing an estimation algorithm's accuracy and in view of downstream tasks, such as its deployment in safety-critical context…
Gaussian processes for dynamics learning in model predictive control
Anna Scampicchio, Elena Arcari, Amon Lahr +1
Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dyn…