arXiv (All)
open access
Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian
The paper proposes a novel data-driven framework for designing and training a feedback linearizing controller by explicitly incorporating relative degree based conditions into the learning process. This enables the conventional feedback controller components to be replaced by neural Lie derivatives,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning neural controllers for nonlinear systems from data#794108
- Learning neural controllers for nonlinear systems from data#790089
- Adaptive Koopman Embedding for Robust Control of Complex Nonlinear Dynamical Systems#1000455
- Physics-Informed Learning of Feedback-Linearizing Representations#1116554
- Neural-NPV Control: Learning Parameter-Dependent Controllers and Lyapunov Functions#614956
Record · ID 1051656
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive