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

2025-05-27

AI Summary

In Industry 4.0 multi-operating-condition process control, reinforcement learning (RL) faces bottlenecks due to its reliance on digital twins and manually engineered reward functions. Method: This paper proposes a novel framework integrating inverse reinforcement learning (IRL) with multi-task learning, introducing implicit contextual variables to automatically identify operational modes from historical closed-loop expert demonstrations—enabling joint learning of mode-specific reward functions and controllers without requiring an accurate process model or prior reward design. Contribution/Results: The method supports unsupervised mode discrimination and adaptive control. Experiments on a continuous stirred-tank reactor (CSTR) and a fed-batch fermentation reactor demonstrate rapid controller mode-switching response, strong cross-mode generalization, and over 37% reduction in steady-state error—significantly enhancing robustness and practicality in intelligent manufacturing scenarios.

Technology Category

Machine Learning: Imitation Learning & Inverse Reinforcement LearningIntelligent Robots: Behavior Learning & ControlPlanning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingResponsible Web: Machine-in-the-loop, human agency and autonomySemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Overcoming dependence on accurate digital twins for process control
• Designing adaptable multi-mode controllers using historical data
• Integrating inverse reinforcement learning with multi-task learning

Innovation

• Multi-task inverse reinforcement learning framework
• Latent-context variable for mode distinction
• Data-driven multi-mode control design