Title :
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
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;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1397-1
DOI :
10.1109/WCICA.2012.6359233
