Related papers: Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning

Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning

URL: http://arxiv.org/abs/2505.21026v1
Date: Tue, 27 May 2025 11:01:00 GMT
Title: Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning
Authors: Runze Lin, Junghui Chen, Biao Huang, Lei Xie, Hongye Su,
Abstract summary: This paper introduces a novel framework that integrates inverse reinforcement learning (IRL) with multi-task learning for data-driven, multi-mode control design.<n>Case studies on a continuous stirred tank reactor and a fed-batch bioreactor validate the effectiveness of this framework in handling adaptable multi-mode data and training controllers.
Score: 11.820381529066475
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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