文献类型：期刊文献

英文题名：A Deep Reinforcement Learning Approach to Improve the Learning Performance in Process Control

作者：Bao, Yaoyao[1];Zhu, Yuanming[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2021

卷号：60

期号：15

起止页码：5504

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20211910308735);WOS:【SCI-EXPANDED(收录号:WOS:000643681300017)】；

基金：This work was supported by the National Natural Science Foundation of China (Basic Science Center Program: 61988101), National Natural Science Fund for Distinguished Young Scholars (61925305), the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under grant B17017, and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Proportional control systems - Deep learning - Controllers - Two term control systems - Process control - Normal distribution

摘要：Advanced model-based control methods have been widely used in industrial process control, but excellent performance requires regular maintenance of its model. Reinforcement learning can online update its policy through the observed data by interacting with the environment. Since a fast and stable learning process is required to improve the adaptability of the controller, we propose an improved deep deterministic actor critic predictor in this paper, where the immediate reward is separated from the action-value function to provide the actor with reliable gradient information at early stages. Then, an expectation form of policy gradient is developed based on the assumption that the state obeys the normal distribution. Simulation results show that the proposed algorithm achieves a more stable and faster learning procedure than those state-of-art deep reinforcement learning (DRL) algorithms. Meanwhile, the obtained policy achieves a more advantageous performance than the fine-tuned proportional integral and derivative (PID) and linear model predictive controllers, especially for those processes with nonlinearity. These indicate that the improved DRL controller has the potential to become an important tool in practical applications.