Developing Purely Data-Driven Multi-Mode Process Controllers Using Inverse Reinforcement Learning

• Published in: Computer Aided Chemical Engineering Vol. 53; pp. 2731 - 2736
• Main Authors: Lin, Runze, Chen, Junghui, Huang, Biao, Xie, Lei, Su, Hongye
• Format: Book Chapter
• Language: English
• Published: 2024
• Subjects: data-driven controller design; inverse reinforcement learning; multi-mode process control; multi-task reinforcement learning; data-driven controller design; inverse reinforcement learning; multi-mode process control; multi-task reinforcement learning
• DOI: 10.1016/B978-0-443-28824-1.50456-7
• ISBN: 9780443288241; 0443288240
• ISSN: 1570-7946

In recent years, process control researchers have been paying close attention to Deep Reinforcement Learning (DRL). DRL offers the potential for model-free controller design, but it is challenging to achieve satisfactory outcomes without accurate simulation models and well-designed reward functions, particularly in multi-mode processes. To address this issue, this paper presents a novel approach that combines inverse RL (IRL) and multi-task learning to provide a purely data-driven solution for multi-mode control design, allowing for transfer learning and adaptation in different operating modes. The effectiveness of this novel approach is demonstrated through a CSTR continuous control case using multi-mode historical closed-loop data. The proposed method offers a promising solution to the challenges of designing controllers for multi-mode processes.