arXiv (All)
open access
Taylor-Informed Indirect Adaptive Predictive Control Using Jacobian-Frozen Affine Predictors
This paper develops a Taylor-informed indirect adaptive predictive control framework for nonlinear sampled-data systems using Jacobian-frozen affine predictors. A finite Taylor expansion approximates the sampled nonlinear dynamics, and recursive least squares (RLS) identifies its polynomial coeffici
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
- Joint Observer-Based Output-Feedback Robust Guidance and Control Funnel Synthesis#1037299
- A Numerical Investigation of Indirect Adaptive Predictive Control with Structure-Informed Nonlinear Regressors#1143686
- Robust variable-gain backstepping control for nonlinear systems with real-time application to induction motor#1000614
- NGMRES convergence analysis and proof of acceleration for contractive and noncontractive iterations#971833
- Exact Average Consensus under Noisy Communication Links: A Decentralized Gradient Perspective#1064626
Record · ID 926276
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