期刊
COMPUTERS IN INDUSTRY
卷 143, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.compind.2022.103748
关键词
Strip rolling; Process control; Reinforcement learning; Ensemble learning
资金
- China Scholarship Council [202006080008]
- National Natural Science Foundation of China [52074085, U21A20117]
- Fundamental Research Funds for the Central Universities [N2004010]
- LiaoNing Revitalization Talents Program [XLYC1907065]
This paper proposes a novel approach that combines ensemble learning with reinforcement learning methods for strip rolling control. The multi-actor PPO method is introduced to improve the control effectiveness. Simulation results demonstrate that the proposed method outperforms conventional control methods and state-of-the-art reinforcement learning methods in terms of process capability and smoothness.
Strip rolling is a typical manufacturing process, in which conventional control approaches are widely applied. Development of the control algorithms requires a mathematical expression of the process by means of the first principles or empirical models. However, it is difficult to upgrade the conventional control approaches in response to the ever-changing requirements and environmental conditions because domain knowledge of control engineering, mechanical engineering, and material science is required. Reinforcement learning is a machine learning method that can make the agent learn from interacting with the environment, thus avoiding the need for the above mentioned mathematical expression. This paper proposes a novel approach that combines ensemble learning with reinforcement learning methods for strip rolling control. Based on the proximal policy optimization (PPO), a multi-actor PPO is proposed. Each randomly initialized actor interacts with the environment in parallel, but only the experience from the actor that obtains the highest reward is used for updating the actors. Simulation results show that the proposed method outperforms the conventional control methods and the state-of-the-art reinforcement learning methods in terms of process capability and smoothness.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据