arXiv (All)
open access
Neural Barriers: An Online Certifiable Learning-enhanced Adaptive High Order Safety Critical Control
Control barrier functions are an effective model-based tool to formally certify the safety of a system. However, transferring their theoretical guarantees to real-world robotics systems requires high model fidelity. For example, payloads or wind disturbances can cause significant model perturbations
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
- Composite Adaptive Control Barrier Functions for Safety-Critical Systems with Parametric Uncertainty#635731
- Composite Adaptive Control Barrier Functions for Safety-Critical Systems with Parametric Uncertainty#633382
- VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search#1210061
- Shielded reinforcement learning for industrial applications: A systematic literature survey#1367258
- Fixed-Time Resilient Integral Reinforcement Learning for Input-Constrained Unknown Nonlinear Systems Under FDI Attacks and Disturbances: A Data-Driven Admissible Warm Start#1083543
Record · ID 1201555
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