Trajectory optimization and positioning control for batch process using learning control

被引：13

作者：

Ruan, Yufei ^{[1
]}

Zhang, Yun ^{[1
]}

Mao, Ting ^{[1
]}

Zhou, Xundao ^{[1
]}

Li, Dequn ^{[1
]}

Zhou, Huamin ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, State Key Lab Mat Proc & Die & Mould Technol, 1037 Luoyu Rd, Wuhan 430074, Hubei, Peoples R China

来源：

CONTROL ENGINEERING PRACTICE | 2019年 / 85卷

基金：

中国国家自然科学基金;

关键词：

Trajectory optimization; Positioning control; Reinforcement learning; Iterative learning control; Batch process; Data-driven; TRACKING; SYSTEMS; ROBOTS;

D O I：

10.1016/j.conengprac.2019.01.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Efficiency and accuracy are critical in the motion control of a batch process. This paper proposes a new intelligent motion control method for a batch process based on reinforcement learning (RL) and iterative learning control (ILC). The proposed learning-based motion control method enables the system to learn from its previous experience. The motion control method can be divided into two parts: (1) RL-based trajectory optimization and (2) ILC-based positioning control. Experiments were conducted to demonstrate the effectiveness of the proposed method. The results indicate that the proposed method not only reduces the process time effectively while ensuring system stability, but also achieves excellent positioning accuracy.

引用

页码：1 / 10

页数：10

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