7 papers
Koopman operator theory: fundamentals, control, and applications
Igor MeziÄ, Jorge Cortés, Karl Worthmann +2
The Koopman operator has gained considerable attention due to its ability to provide a global linear representation of highly complex dynamical systems. The operator describes nonl…
SAD-Flower: Flow Matching for Safe, Admissible, and Dynamically Consistent Planning
Tzu-Yuan Huang, Armin Lederer, Dai-Jie Wu +6
Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action constraints, whose satisfaction…
Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models
Lei Zheng, Peiqi Yu, Zengqi Peng +2
Diffusion models excel at generating diverse and multimodal trajectories for robotic planning, yet their iterative denoising process introduces latency that is incompatible with hi…
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…
Safe Event-triggered Gaussian Process Learning for Barrier-Constrained Control
Armin Lederer, Azra BegzadiÄ, Sandra Hirche +2
While control barrier functions (CBFs) are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical lim…
Toward Near-Globally Optimal Nonlinear Model Predictive Control via Diffusion Models
Tzu-Yuan Huang, Armin Lederer, Nicolas Hoischen +4
Achieving global optimality in nonlinear model predictive control (NMPC) is challenging due to the non-convex nature of the underlying optimization problem. Since commonly employed…