Deep Reinforcement Learning for Process Control: A Primer for Beginners

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.

21 pages

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Continuous control with deep reinforcement learning
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Playing Atari with Deep Reinforcement Learning
• Policy Gradient Methods for Off-policy Control

Cited by in corpus (12)

• Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
• Machine learning for industrial sensing and control: A survey and practical perspective
• Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control
• Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
• Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
• Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
• A Meta-Reinforcement Learning Approach to Process Control
• Reinforcement Learning based Design of Linear Fixed Structure Controllers
• Constrained Model-Free Reinforcement Learning for Process Optimization
• Reinforcement Learning with Partial Parametric Model Knowledge
• Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering
• Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)