.png){kind=icon}
返回
Linear programming and model predictive control
.png){kind=icon}
PRE
AI
DOI：10.1016/S0959-1524(99)00034-7.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The practicality of model predictive control (MPC) is partially limited by the ability to solve optimization problems in real time. This requirement limits the viability of MPC as a control strategy for large scale processes. One strategy for improving the computational performance is to formulate MPC using a linear program. While the linear programming formulation seems appealing from a numerical standpoint, the controller does not necessarily yield good closed-loop performance. In this work, we explore MPC with an II performance criterion. We demonstrate how the non-smoothness of the objective function may yield either dead-beat or idle control performance. (C) 2000 IFAC. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
model predictive control
linear programming
optimization
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:
9.2K
机构
暂无机构信息
引用论文
.png){kind=icon}
Absolute Bioavailability of Ponesimod, a Selective S1P1 Receptor Modulator, in Healthy Male Subjects
PREAI
分享收藏
查看更多内容