Towards Self‐Driving Processes: A Deep Reinforcement Learning Approach to Control

Steven Spielberg, Aditya Tulsyan, Nathan P. Lawrence, Philip D Loewen, R. Bhushan Gopaluni
2019 AIChE Journal  
Advanced model-based controllers are well established in process industries. However, such controllers require regular maintenance to maintain acceptable performance. It is a common practice to monitor controller performance continuously and to initiate a remedial model re-identification procedure in the event of performance degradation. Such procedures are typically complicated and resource-intensive, and they often cause costly interruptions to normal operations. In this paper, we exploit
more » ... nt developments in reinforcement learning and deep learning to develop a novel adaptive, model-free controller for general discrete-time processes. The DRL controller we propose is a data-based controller that learns the control policy in real time by merely interacting with the process. The effectiveness and benefits of the DRL controller are demonstrated through many simulations.
doi:10.1002/aic.16689 fatcat:2po2quoyfrakpmqcfcsfp6tare