arXiv (All)
open access
AccelMPC: High-Rate, Low-Power FPGA-Accelerated Model Predictive Control for Tiny Drones
Unlocking the potential of tiny aerial robots requires order of magnitude improvements in the performance of embedded edge control. In particular, although recent cached model predictive control (MPC) solvers can handle the fast system dynamics and complex constraints required for agile drone flight
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#366257
- Learning-enabled Acceleration of Scenario-based Model Predictive Control#973271
- Contact-Aware Incremental Model Predictive Control for an Underactuated Aerial Manipulator#999359
- Data and code supporting the PhD Thesis: Learning-based model predictive control for mixed-logical dynamical and linear systems#26408
- Data and code supporting the PhD Thesis: Learning-based model predictive control for mixed-logical dynamical and linear systems#26407
Record · ID 987133
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