DAIS Lab Publications

1. Journal Paper Top 10% Most Downloaded
   Towards Self-Driving Processes: A Deep Reinforcement Learning Approach to Control
   Steven P. Spielberg, Aditya Tulsyan, Nathan P. Lawrence, Philip D. Loewen, R. Bhushan Gopaluni
   AIChE Journal. 2019 [PDF]
2. Conference Proceedings NeurIPS Spotlight
   Almost Surely Stable Deep Dynamics
   Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backstrom and R. Bhushan Gopaluni
   In Proceedings of Advances in Neural Information Processing Systems 33 (NeurIPS 2020). 2020 [PDF] [Code] [Poster] [Video] [arXiv]
3. Conference Proceedings
   Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
   Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen, Michael G. Forbes, Johan U. Backstrom, R. Bhushan Gopaluni
   In Proceedings of the 21st IFAC World Congress. 2020 [PDF] [Video] [arXiv]
4. Conference Proceedings
   Reinforcement Learning based Design of Linear Fixed Structure Controllers
   Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen, Michael G. Forbes, Johan U. Backstrom, R. Bhushan Gopaluni
   In Proceedings of the 21st IFAC World Congress. 2020 [PDF] [Video] [arXiv]
5. Conference Proceedings
   Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
   R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat, Biao Huang, Jong Min Lee, Faraz Amjad, Seshu Kumar Damarla, Jong Woo Kim, Nathan P. Lawrence
   In Proceedings of the 21st IFAC World Congress. 2020 [PDF] [Supplementary Info]
6. Conference Proceedings Keynote Presentation
   A Meta-Reinforcement Learning Approach to Process Control
   Daniel G. McClement, Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backstrom and R. Bhushan Gopaluni
   In Proceedings of the 16th IFAC International Symposium on Advanced Control of Chemical Processes (ADCHEM 2021). 2021 [PDF] [Slides] [Video] [arXiv]
7. Journal Paper
   Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
   Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen, Daniel G. McClement, Johan U. Backström, R. Bhushan Gopaluni
   Control Engineering Practice. 2021 [PDF]
8. Conference Proceedings
   Meta-Reinforcement Learning for Adaptive Control of Second Order Systems
   Daniel G. McClement, Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen, Johan U. Backström, R. Bhushan Gopaluni
   In Proceedings of the 7th International Symposium on Advanced Control of Industrial Processes (AdCONIP). 2022 [PDF] [Video]
9. Journal Paper
   Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
   Daniel G. McClement, Nathan P. Lawrence, Johan U. Backström, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni
   Journal of Process Control. 2022 [PDF]
10. Conference Proceedings
    A modular framework for stabilizing deep reinforcement learning control
    Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni
    In Proceedings of the 22nd IFAC World Congress. 2023 [PDF] [Slides] [Video] [arXiv]
11. Conference Proceedings
    Reinforcement Learning with Partial Parametric Model Knowledge
    Shuyuan Wang, Philip D. Loewen, Nathan P. Lawrence, Michael G. Forbes, R. Bhushan Gopaluni
    In Proceedings of the 22nd IFAC World Congress. 2023 [PDF] [Video] [arXiv]
12. Journal Paper
    Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
    Tobi Michael Alabi, Nathan P. Lawrence, Lin Lu, Zaiyue Yang, R. Bhushan Gopaluni
    Applied Energy. 2023 [PDF]
13. Conference Proceedings
    Deep Hankel matrices with random elements
    Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni
    In Proceedings of the 6th Annual Learning for Dynamics & Control Conference (L4DC). 2024 [PDF]
14. Conference Proceedings
    Guiding Reinforcement Learning with Incomplete System Dynamics
    Shuyuan Wang, Jingliang Duan, Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni, Lixian Zhang
    In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024, To Appear). 2024 [PDF] [Video] [Presentation] [arXiv]
15. Journal Paper
    Machine learning for industrial sensing and control: A survey and practical perspective
    Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim, Aditya Tulsyan, Faraz Amjad, Kai Wang, Benoit Chachuat, Jong Min Lee, Biao Huang, R. Bhushan Gopaluni
    Control Engineering Practice. 2024 [PDF]
16. Journal Paper
    Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior
    Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni
    Automatica. 2024 [PDF] [arXiv]
17. Patent
    Method and system for directly tuning PID parameters using a simplified actor-critic approach to reinforcement learning
    Nathan Lawrence, Philip D. Loewen, Bhushan Gopaluni, Gregory E. Stewart
    U.S. Patent No. 11,500,337. 2022
18. Patent
    Application of simple random search approach for reinforcement learning to controller tuning parameters
    Nathan Lawrence, Philip D. Loewen, Bhushan Gopaluni, Gregory E. Stewart
    U.S. Patent No. 11,307,562. 2022
19. Patent
    Process controller with meta-reinforcement learning
    Daniel McClement, Nathan Lawrence, Philip Loewen, Bhushan Gopaluni, Michael Forbes, and Johan Backstroem
    U.S. Patent Application No. 17/653,175.. 2022