Paper Info
Title
Cost guaranteed robust sampled-data parallel model design using polynomial approach
Abstract
The multivariable H2 guaranteed robust minimum variance parallel model design problem subjected to norm bounded uncertainties is studied in this paper for sampled-data systems. It consists of two paths connected in parallel with a common stationary stochastic input. One of them has an unknown system to be designed despite the presence of disturbances, so that the output signal of the two paths is of minimum variance. The systems and noise models are assumed to be represented by polynomial matrices that are not perfectly known except that they belong to a certain set. The sampled-data design is based on a fast sampling and lifting technique resulting on a finite-dimensional filter. An application case of robust parallel model design to the feedforward load-frequency control on hydro-generating units is provided.
Year
DOI
Venue
2005
10.1016/j.sigpro.2004.11.013
Signal Processing
Keywords
Field
DocType
polynomial approach,sampled data,minimum variance parallel model,common stationary stochastic input,application case,filtering,design problem,certain set,robust,noise model,sampled-data system,robust sampled-data parallel model,feedforward,sampled-data design,minimum variance,robust parallel model design
Minimum-variance unbiased estimator,Mathematical optimization,Multivariable calculus,Polynomial,Control theory,Matrix (mathematics),Systems design,Filter (signal processing),Mathematics,Feed forward,Bounded function
Journal
Volume
Issue
ISSN
85
4
Signal Processing
Citations 
PageRank 
References 
2
0.41
11
Authors
2
Name
Order
Citations
PageRank
Ruben H. Milocco183.53
C. Muravchik254368.59