Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908381356228608 |
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| author | Lin, Runze Chen, Junghui Huang, Biao Xie, Lei Su, Hongye |
| author_facet | Lin, Runze Chen, Junghui Huang, Biao Xie, Lei Su, Hongye |
| contents | In the era of Industry 4.0 and smart manufacturing, process systems engineering must adapt to digital transformation. While reinforcement learning offers a model-free approach to process control, its applications are limited by the dependence on accurate digital twins and well-designed reward functions. To address these limitations, this paper introduces a novel framework that integrates inverse reinforcement learning (IRL) with multi-task learning for data-driven, multi-mode control design. Using historical closed-loop data as expert demonstrations, IRL extracts optimal reward functions and control policies. A latent-context variable is incorporated to distinguish modes, enabling the training of mode-specific controllers. Case studies on a continuous stirred tank reactor and a fed-batch bioreactor validate the effectiveness of this framework in handling multi-mode data and training adaptable controllers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21026 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning Lin, Runze Chen, Junghui Huang, Biao Xie, Lei Su, Hongye Systems and Control Artificial Intelligence Machine Learning In the era of Industry 4.0 and smart manufacturing, process systems engineering must adapt to digital transformation. While reinforcement learning offers a model-free approach to process control, its applications are limited by the dependence on accurate digital twins and well-designed reward functions. To address these limitations, this paper introduces a novel framework that integrates inverse reinforcement learning (IRL) with multi-task learning for data-driven, multi-mode control design. Using historical closed-loop data as expert demonstrations, IRL extracts optimal reward functions and control policies. A latent-context variable is incorporated to distinguish modes, enabling the training of mode-specific controllers. Case studies on a continuous stirred tank reactor and a fed-batch bioreactor validate the effectiveness of this framework in handling multi-mode data and training adaptable controllers. |
| title | Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning |
| topic | Systems and Control Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.21026 |