arXiv (All)
open access
Data-Driven Switched State Estimation with Sparse Sensor Scheduling for Nonlinear Networked Systems
This paper presents a data-driven framework for joint observer design and sparse sensor scheduling for unknown nonlinear networked systems. The nonlinear dynamics are approximated online as a piecewise sequence of locally linearized discrete-time systems, resulting in a switched linear representatio
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
- Event-Triggered Practical Fixed-Time Integral Reinforcement Learning for Unknown Nonlinear Systems#1155735
- Robust Closed-Form Control for MIMO Nonlinear Systems under Generalized Conflicting Time-Varying Hard and Soft Constraints (extended version)#924464
- Robust Closed-Form Control for MIMO Nonlinear Systems under Generalized Conflicting Time-Varying Hard and Soft Constraints (extended version)#920233
- "Local/Remote" Forwarding Decision in Switched Data Link Subnetworks#132534
- On Data-Driven Model Identification for Nonlinear Optimal Control#1036510
Record · ID 1105730
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