arXiv (All)
open access
SD-DPC: Sparse Dictionary Differentiable Predictive Control
We present sparse dictionary differentiable predictive control (SD-DPC), a framework for learning sparse, interpretable feedback policies for nonlinear systems from data. A prediction model is first identified by rollout-based sparse identification of nonlinear dynamics (SINDy), building on gradient
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
- Exploiting Differential Flatness for Efficient Learning-based Model Predictive Control of Constrained Multi-Input Control Affine Systems#138913
- Hybrid Model Predictive Control with Physics-Informed Neural Network for Satellite Attitude Control#927240
- Parallel Policy-Gradient Methods for Parameter Optimization of Nonlinear Feedback Controllers#1083585
- 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 1186780
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