.png){kind=icon}
返回
Iterative learning model predictive control for multi-phase batch processes
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jprocont.2007.10.014.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-phase batch process is common in industry, such as injection molding process, fermentation and sequencing batch reactor; however, it is still an open problem to control and analyze this kind of processes. Motivated by injection molding processes, the multi-phase batch process in each cycle is formulated as a switched system with internally forced switching instant. Controlling multi-phase batch processes can be decomposed into two subtasks: detecting the dynamics-switching-time; designing the control law for each phase with considering switching effect. In this paper, it is assumed that the dynamics-switching-time can be obtained in real-time and only the second subtask is studied. To exploit the repetitive nature of batch processes, iterative learning control scheme is used in batch direction. To deal with constraints, updating law is designed by using model predictive control scheme. An online iterative learning model predictive control (ILMPC) law is first proposed with a quadratic programming problem to be solved online. To reduce computation burden, an offline ILMPC is also proposed and compared. Applications on injection molding processes show that the proposed algorithms can control multi-phase batch processes well. (C) 2007 Elsevier Ltd. All rights reserved.
Keyword:
multi-phase batch process
iterative learning control
model predictive control
constraint
quadratic programming
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
3.5K
被引数:
7.3K
机构
引用论文
.png){kind=icon}
分享收藏Single-cycle and multi-cycle generalized 2D model predictive iterative learning control (2D-GPILC) schemes for batch processes间歇过程的单周期和多周期广义2D模型预测迭代学习控制 (2D-GPILC) 方案
PREAI
分享收藏Robust fault-tolerant control of a class of non-minimum phase nonlinear processes一类非最小相位非线性过程的鲁棒容错控制
PREAI
分享收藏
查看更多内容