arXiv (All)
open access
Augmenting PID Control with Deep Reinforcement Learning: A Hybrid Approach to the Industrial Benchmark
As industrial processes grow in complexity, traditional Proportional-Integral-Derivative (PID) controllers are often insufficient for handling their non-linear, multi-input dynamics. We propose using advanced Deep Reinforcement Learning (DRL) to prove its advantages in these complex environments. To
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
- Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning#1119889
- ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback#799059
- ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback#797636
- Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems#381723
- Contraction-Aware Reinforcement Learning for Nonlinear Control with Statistical Robustness#611317
Record · ID 1037267
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