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 recent 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.
@article{arxiv.2004.05490,
title = {Deep Reinforcement Learning for Process Control: A Primer for Beginners},
author = {Steven Spielberg and Aditya Tulsyan and Nathan P. Lawrence and Philip D Loewen and R. Bhushan Gopaluni},
journal= {arXiv preprint arXiv:2004.05490},
year = {2020}
}