3 citations · 4 across the 10 of their papers we have counts for
3 papers · 1 filter
Demystifying Linear Operator Learning for Control Systems
Max Beier, Nicolas Hoischen, Sandra Hirche +1
This paper proposes a structured approach to learning linear operators for control systems from data. We address both structural and learning-theoretic aspects of the problem. To d…
Task-Oriented Active Learning of Residual Dynamics for Model Predictive Path Integral Control
Nobuaki Aoki, Hojin Lee, Stefan Sosnowski +1
Online residual learning can reduce model mismatch in predictive control, but passive data collection may fail to adequately cover states that become important later in the task. T…
On the Existence of Quadratic Control Lyapunov Functions for Koopman-Operator based Bilinear Systems
Sami Leon Noel Aziz Hanna, Nicolas Hoischen, Sandra Hirche +1
Koopman operator-based methods enable data-driven bilinear representations of unknown nonlinear control systems. Accurate representations often demand significantly higher dimensio…