.png){kind=icon}
返回
Efficient robust predictive control
.png){kind=icon}
PRE
AI
DOI：10.1109/9.871769.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Predictive constrained control of time-varying and/or uncertain linear systems has been effected through the use of ellipsoidal invariant sets [6] Linear matrix inequalities (LMI's) have been used to design a state-dependent state-feedback law that maintains the state vector inside invariant feasible sets. For the purposes of prediction however, at each time instant, the state feedback law is assumed constant. In addition, due to the large number of LMI's involved, online computation becomes intractable for anything other than small dimensional systems. Here we propose a new approach that deploys a fixed state-feedback law but introduces extra degrees of freedom through the use of perturbations on the fixed state-feedback law. The problem is so formulated that all demanding computations can be performed offline leaving only a simple optimization problem to be solved online. Over and above the very significant reduction in computational cost, the extra degrees of freedom allow for better performance and wider applicability.
Keyword:
constrained
predictive control
robust
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:
7
论文数:
1.3W
被引数:
7.4W
机构
暂无机构信息
引用论文
A numerically robust state-space approach to stable-predictive control strategies.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏分享收藏The reaction of 5-acetyl-2-aminooxazole with amines: an approach to 1H-5-acetyl-2-aminoimidazoles
PREAI
分享收藏 没有更多内容
没有更多内容