arXiv (All)
open access
Collective Tube Model Predictive Control With Distribution-Free Joint Safety Certificates
Data-calibrated stochastic MPC typically builds separate risk margins for many events along the horizon, such as times, facets, state/input components, or obstacles, and then combines them with a union bound. This approach is valid, but it does not match the key object used in the tube-MPC recursive
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-enabled Acceleration of Scenario-based Model Predictive Control#973271
- Model Predictive Control with Multiple Constraint Horizons#1012078
- AI Safety: Not Optional, Not Later#999906
- Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning#1034723
- Predicted-Flow Control Barrier Functions for Real-Time Safe Optimal Control#1049858
Record · ID 1062236
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