Process Control

Process control aims to maintain desired system outputs by manipulating inputs, a challenge amplified by complex, nonlinear systems common in manufacturing and other industries. Current research emphasizes integrating machine learning, particularly reinforcement learning and deep learning models, with traditional control methods to handle model uncertainty and optimize control strategies, often incorporating Bayesian inference or transfer learning techniques for improved efficiency and robustness. These advancements are crucial for enhancing safety, efficiency, and quality in diverse applications, from chemical processing to robotics, by enabling more adaptive and reliable control systems.