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Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control
Runze Lin, Ziqi Zhuo, Junghui Chen, Lei Xie, Hongye Su
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 93%
Last extracted: 7/13/2026, 3:32:57 AM
Summary
This paper proposes an Iterative Learning Control-Informed Reinforcement Learning (IL-CIRL) framework to address safety and stability issues in Deep Reinforcement Learning (DRL) for batch process control. By integrating Kalman filter-based state estimation within an iterative learning structure, the method guides DRL agents to satisfy operational constraints and ensure stability in dual-layer batch-to-batch and within-batch control architectures.
Entities (5)
Relation Signals (5)
IL-CIRL → informedby → Iterative Learning Control
confidence 95% · Iterative Learning Control-Informed Reinforcement Learning
IL-CIRL → integrates → Deep Reinforcement Learning
confidence 95% · This study introduces an Iterative Learning Control-Informed Reinforcement Learning (IL-CIRL) framework for training DRL controllers
IL-CIRL → uses → Kalman Filter
confidence 92% · The proposed method incorporates Kalman filter-based state estimation within the iterative learning structure
IL-CIRL → appliedto → Batch Process Control
confidence 90% · for batch processes operating under multiple disturbance conditions
Deep Reinforcement Learning → suffersfrom → stochastic uncertainty
confidence 90% · A significant limitation of Deep Reinforcement Learning (DRL) is the stochastic uncertainty in actions
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Abstract
Abstract:A significant limitation of Deep Reinforcement Learning (DRL) is the stochastic uncertainty in actions generated during exploration-exploitation, which poses substantial safety risks during both training and deployment. In industrial process control, the lack of formal stability and convergence guarantees further inhibits adoption of DRL methods by practitioners. Conversely, Iterative Learning Control (ILC) represents a well-established autonomous control methodology for repetitive systems, particularly in batch process optimization. ILC achieves desired control performance through iterative refinement of control laws, either between consecutive batches or within individual batches, to compensate for both repetitive and non-repetitive disturbances. This study introduces an Iterative Learning Control-Informed Reinforcement Learning (IL-CIRL) framework for training DRL controllers in dual-layer batch-to-batch and within-batch control architectures for batch processes. The proposed method incorporates Kalman filter-based state estimation within the iterative learning structure to guide DRL agents toward control policies that satisfy operational constraints and ensure stability guarantees. This approach enables the systematic design of DRL controllers for batch processes operating under multiple disturbance conditions.
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- Source: https://arxiv.org/abs/2603.15180v1
- Canonical: https://arxiv.org/abs/2603.15180v1
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Skip to main content arXiv is now an independent nonprofit! Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Systems and Control arXiv:2603.15180v1 (eess) [Submitted on 16 Mar 2026] Title:Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control Authors:Runze Lin, Ziqi Zhuo, Junghui Chen, Lei Xie, Hongye Su View a PDF of the paper titled Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control, by Runze Lin and 4 other authors View PDF Abstract:A significant limitation of Deep Reinforcement Learning (DRL) is the stochastic uncertainty in actions generated during exploration-exploitation, which poses substantial safety risks during both training and deployment. In industrial process control, the lack of formal stability and convergence guarantees further inhibits adoption of DRL methods by practitioners. Conversely, Iterative Learning Control (ILC) represents a well-established autonomous control methodology for repetitive systems, particularly in batch process optimization. ILC achieves desired control performance through iterative refinement of control laws, either between consecutive batches or within individual batches, to compensate for both repetitive and non-repetitive disturbances. This study introduces an Iterative Learning Control-Informed Reinforcement Learning (IL-CIRL) framework for training DRL controllers in dual-layer batch-to-batch and within-batch control architectures for batch processes. The proposed method incorporates Kalman filter-based state estimation within the iterative learning structure to guide DRL agents toward control policies that satisfy operational constraints and ensure stability guarantees. This approach enables the systematic design of DRL controllers for batch processes operating under multiple disturbance conditions. Subjects: Systems and Control (eess.SY); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.15180 [eess.SY] (or arXiv:2603.15180v1 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2603.15180 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Runze Lin [view email] [v1] Mon, 16 Mar 2026 12:17:43 UTC (2,178 KB) Full-text links: Access Paper: View a PDF of the paper titled Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control, by Runze Lin and 4 other authorsView PDF view license Current browse context: eess.SY < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.SY eess References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from