URN to cite this document: urn:nbn:de:bvb:703-epub-8178-0
Title data
Grüne, Lars ; Sperl, Mario ; Chatterjee, Debasish:
Representation of practical nonsmooth control Lyapunov functions by piecewise affine functions and neural networks.
Bayreuth
,
2025
. - 9 S.
This is the latest version of this item.
![]() |
|
||||||||
Download (490kB)
|
Project information
Project title: |
Project's official title Project's id Curse-of-dimensionality-free nonlinear optimal feedback control with deep neural networks. A compositionality-based approach via Hamilton-Jacobi-Bellman PDEs 463912816 |
---|---|
Project financing: |
Deutsche Forschungsgemeinschaft |
Abstract
In this paper we give conditions under which control Lyapunov functions exist that can be represented by either piecewise affine functions or by neural networks with a suitable number of ReLU layers. The results provide a theoretical foundation for recent computational approaches for computing control Lyapunov functions with optimization-based and machine-learning techniques.
Further data
Available Versions of this Item
-
Representation of practical nonsmooth control Lyapunov functions by piecewise affine functions and neural networks. (deposited 13 Dec 2024 08:16)
- Representation of practical nonsmooth control Lyapunov functions by piecewise affine functions and neural networks. (deposited 10 Feb 2025 07:35) [Currently Displayed]