A new estimate method for linear constrained systems

Author

Wen, Chuanbo ; Cai, Yunze ; Xu, Xiaoming

Author_Institution

Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China

fYear

2012

fDate

6-8 July 2012

Firstpage

4463

Lastpage

4466

Abstract

This paper deals with the problem of optimal filtering for linear discrete constrained dynamic systems. The constraint matrix and constraint vector in this system are allowed to vary not only in the value but also in the dimension. Firstly, the original constrained state is transformed into a new reduced state model without constraint. Then, the prediction of the reduced state is given by using the least square method. Finally, the optimal estimate of original state is produced by the update process similar to the Kalman filter. A numerical example is presented to demonstrate the effectiveness of the new method.

Keywords

discrete systems; filtering theory; least squares approximations; linear systems; matrix algebra; prediction theory; state estimation; vectors; constraint matrix; constraint vector; least square method; linear discrete constrained dynamic systems; optimal filtering; optimal original state estimation; reduced state model; reduced state prediction; Educational institutions; Equations; Kalman filters; Least squares methods; Mathematical model; Noise; Vehicles; Constraint filtering; Estimate; Least square method; State transformation;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2012 10th World Congress on

Conference_Location

Beijing

Print_ISBN

978-1-4673-1397-1

Type

conf

DOI

10.1109/WCICA.2012.6359233

Filename

6359233