arXiv (All)
open access
Learning stabilising policies for constrained nonlinear systems
This work proposes a two-layered control scheme for constrained nonlinear systems represented by a class of recurrent neural networks and affected by additive disturbances. In particular, a base controller ensures global or regional closed-loop l_p-stability of the error in tracking a desired equili
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
- Contraction-Aware Reinforcement Learning for Nonlinear Control with Statistical Robustness#612794
- Contraction-Aware Reinforcement Learning for Nonlinear Control with Statistical Robustness#611317
- EasyPPO: Stabilizing the Critic Is Key#1132003
- MPC for underactuated spacecraft control with a Lyapunov supervised physics-informed neural network correction layer#630039
- MPC for underactuated spacecraft control with a Lyapunov supervised physics-informed neural network correction layer#628177
Record · ID 820278
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