Improved robustness of multivariable model predictive control under model uncertainties

被引：0
|
作者：
Stoica, Cristina [1 ]
Rodriguez-Ayerbe, Pedro [1 ]
Dumur, Didier [1 ]
机构：
[1] Supelec, Dept Automat Control, F-91192 Gif Sur Yvette, France
来源：
ICINCO 2007: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL SPSMC: SIGNAL PROCESSING, SYSTEMS MODELING AND CONTROL | 2007年
关键词：
model predictive control (MPC); multivariable systems; linear matrix inequalities; robust control;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a state-space methodology for enhancing the robustness of multivariable MPC controlled systems through the convex optimization of a multivariable Youla parameter. The procedure starts with the design of an initial stabilizing Model Predictive Controller in the state-space representation, which is then robustified under modeling errors considered as unstructured uncertainties. The resulting robustified MIMO control law is finally applied to the model of a stir-red tank reactor to reduce the impact of measurement noise and modelling errors on the system.
引用
收藏
页码：283 / 288
页数：6
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