.png){kind=icon}
返回
Optimal Iterative Learning Control Based on a Time-Parametrized Linear Time-Varying Model for Batch Processes
.png){kind=icon}
PRE
AI
DOI：10.1021/ie302561t.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, an optimal iterative learning control (ILC) algorithm based on a time-parametrized linear time-varying (LTV) model for batch processes is proposed. Utilizing the repetitive nature of batch processes, a time-parametrized LTV model is used to represent the nonlinear behavior, with its consistence and variance properties established. Furthermore, an optimal ILC algorithm based on the time-parametrized LTV model is developed, and its convergence property is analyzed. Simulations have demonstrated the effectiveness and excellent performance of the proposed method.
Keyword:
NEURAL-NETWORK MODELS
PREDICTIVE CONTROL
IDENTIFICATION
SYSTEMS
LPV
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
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
机构
引用论文
分享收藏Survey on iterative learning control, repetitive control, and run-to-run control迭代学习控制、重复控制和运行间控制综述
PREAI
分享收藏Kernel methods for subspace identification of multivariable LPV and bilinear systems.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
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
查看更多内容