Toward self‐driving processes: A deep reinforcement learning approach to control

TLDR

Model‑based controllers are common in process industries but require regular maintenance and costly, resource‑intensive re‑identification when performance degrades. The paper develops a novel adaptive, model‑free controller for general discrete‑time processes using reinforcement learning and deep learning. The controller learns a real‑time control policy by interacting with the process, relying solely on data without a prior model. Simulations show the DRL controller’s effectiveness and benefits.

Abstract

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.