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

TL;DR

This paper presents a novel multi-task inverse reinforcement learning framework for multi-mode process control, enabling adaptive and data-driven control strategies in smart manufacturing environments.

Contribution

It introduces a new IRL-based approach with mode discrimination for designing flexible controllers without relying on digital twins.

Findings

Effective multi-mode control demonstrated on chemical reactors

IRL extracts reward functions from historical data

Controllers adapt to different process modes

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.