Self-Optimizing Control of Continuous Processes Based on Reinforcement Learning

Abstract

This paper addresses the Self-Optimizing Control (SOC) problem in industrial continuous processes and proposes a Reinforcement-Learning (RL)-based SOC approach to improve dynamic performance under high-frequency disturbances. In the proposed framework, the SOC controlled variable structure is embedded in the Actor network, and reward functions are designed based on economic indicators. Through interaction with the environment, the RL agent optimizes controlled variables while implicitly considering implementability and steady-state uniqueness. Online fine-tuning is further introduced to alleviate model mismatch. Experiments on a continuous stirred-tank reactor with disturbances compare the proposed RL-based SOC method with the Objective-Guided Controlled Variable Learning Approach based on steady-state data. The results show that the RL method achieves improved dynamic performance under real-time disturbances, generates smooth controlled variable outputs without explicit regularization, reduces hyperparameter-tuning complexity, and enhances adaptability through online adjustment. Overall, the proposed RL-based SOC approach provides an effective solution for nonlinear process control and offers a promising reference for future studies involving multiple disturbances, multiple operating conditions, and model-free scenarios.