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Nathan P. Lawrence
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Toward self‐driving processes: A deep reinforcement learning approach to control
S Spielberg, A Tulsyan, NP Lawrence, PD Loewen, R Bhushan Gopaluni
AIChE Journal 65 (10), e16689, 2019
183*2019
Deep reinforcement learning with shallow controllers: An experimental application to PID tuning
NP Lawrence, MG Forbes, PD Loewen, DG McClement, JU Backström, ...
Control Engineering Practice 121, 105046, 2022
932022
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
RB Gopaluni, A Tulsyan, B Chachuat, B Huang, JM Lee, F Amjad, ...
IFAC-PapersOnLine 53, 218-229, 2020
49*2020
Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
TM Alabi, NP Lawrence, L Lu, Z Yang, RB Gopaluni
Applied Energy 333, 120633, 2023
322023
Meta-reinforcement learning for the tuning of PI controllers: An offline approach
DG McClement, NP Lawrence, JU Backström, PD Loewen, MG Forbes, ...
Journal of Process Control 118, 139-152, 2022
292022
Optimal PID and antiwindup control design as a reinforcement learning problem
NP Lawrence, GE Stewart, PD Loewen, MG Forbes, JU Backstrom, ...
IFAC-PapersOnLine 53, 236-241, 2020
282020
Almost Surely Stable Deep Dynamics
NP Lawrence, PD Loewen, MG Forbes, JU Backstrom, RB Gopaluni
Advances in Neural Information Processing Systems 33, 18942--18953, 2020
252020
Machine learning for industrial sensing and control: A survey and practical perspective
NP Lawrence, SK Damarla, JW Kim, A Tulsyan, F Amjad, K Wang, ...
Control Engineering Practice 145, 105841, 2024
222024
Reinforcement Learning based Design of Linear Fixed Structure Controllers
NP Lawrence, GE Stewart, PD Loewen, MG Forbes, JU Backstrom, ...
IFAC-PapersOnLine 53, 230-235, 2020
112020
A meta-reinforcement learning approach to process control
DG McClement, NP Lawrence, PD Loewen, MG Forbes, JU Backström, ...
IFAC-PapersOnLine 54 (3), 685-692, 2021
82021
Meta-reinforcement learning for adaptive control of second order systems
DG McClement, NP Lawrence, MG Forbes, PD Loewen, JU Backström, ...
2022 IEEE International Symposium on Advanced Control of Industrial …, 2022
42022
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior
NP Lawrence, PD Loewen, S Wang, MG Forbes, RB Gopaluni
Automatica 164, 111642, 2024
22024
A modular framework for stabilizing deep reinforcement learning control
NP Lawrence, PD Loewen, S Wang, MG Forbes, RB Gopaluni
IFAC-PapersOnLine 56 (2), 8006-8011, 2023
22023
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
R Bhushan Gopaluni, A Tulsyan, B Chachuat, B Huang, JM Lee, F Amjad, ...
arXiv e-prints, arXiv: 2209.11123, 2022
22022
Guiding reinforcement learning with incomplete system dynamics
S Wang, J Duan, NP Lawrence, PD Loewen, MG Forbes, RB Gopaluni, ...
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2024
12024
Deep reinforcement learning agents for industrial control system design
NP Lawrence
University of British Columbia, 2023
12023
A view on learning robust goal-conditioned value functions: Interplay between RL and MPC
NP Lawrence, PD Loewen, MG Forbes, RB Gopaluni, A Mesbah
arXiv preprint arXiv:2502.06996, 2025
2025
Deep Hankel matrices with random elements
N Lawrence, P Loewen, S Wang, M Forbes, B Gopaluni
6th Annual Learning for Dynamics & Control Conference, 1579-1591, 2024
2024
Reinforcement Learning with Partial Parametric Model Knowledge
S Wang, PD Loewen, NP Lawrence, MG Forbes, RB Gopaluni
IFAC-PapersOnLine 56 (2), 8012-8017, 2023
2023
Method and system for directly tuning PID parameters using a simplified actor-critic approach to reinforcement learning
N Lawrence, PD Loewen, B Gopaluni, GE Stewart
US Patent 11,500,337, 2022
2022
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