Reduced order controllers for distributed parameter systems: LQG balanced truncation and an adaptive approach

被引：17
|
作者：
King, Belinda Batten [1 ]
Hovakimyan, Naira
Evans, Katie A.
Buhl, Michael
机构：
[1] Oregon State Univ, Dept Mech Engn, Corvallis, OR 97331 USA
[2] Virginia Polytech Inst & State Univ, Dept Aerosp & Ocean Engn, Blacksburg, VA 24061 USA
[3] Tech Univ Munich, Dept Aerosp Engn, D-8000 Munich, Germany
来源：
MATHEMATICAL AND COMPUTER MODELLING | 2006年 / 43卷 / 9-10期
基金：
美国国家科学基金会;
关键词：
reduced order control; balanced truncation; LQG balancing; neural networks; output feedback; distributed parameter systems;
D O I：
10.1016/j.mcm.2005.05.031
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, two methods are reviewed and compared for designing reduced order controllers for distributed parameter systems. The first involves a reduction method known as LQG balanced truncation followed by MinMax control design and relies on the theory and properties of the distributed parameter system. The second is a neural network based adaptive output feedback synthesis approach, designed for the large scale discretized system and depends upon the relative degree of the regulated outputs. Both methods are applied to a problem concerning control of vibrations in a nonlinear structure with a bounded disturbance. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1136 / 1149
页数：14
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