arXiv (All)
open access
Asynchronous Sensitivity-Based Distributed NMPC
This paper presents a cooperative distributed model predictive control (MPC) scheme for nonlinear continuous-time systems. The centralized optimal control problem is solved asynchronously via a fixed number of sensitivity-based distributed programming (SBDP) iterations. The proposed scheme requires
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
- A Scalable Learned Iterative Solver for Nonlinear MPC via Temporal Decomposition#1297832
- Distributed Model Predictive Control with Connectivity-based Contracts#1014958
- A Data-Driven Distributed Control Scheme: Learning Multi-Objective Agent-Based MPC for Path-Tracking#1000108
- Distributed Model Predictive Control for Optimal Consensus of Constrained Heterogeneous Multi-agent Systems#784934
- Distributed Model Predictive Control for Optimal Consensus of Constrained Heterogeneous Multi-agent Systems#783344
Record · ID 1458374
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