Wiener State Estimator for Non-regular Descriptor System

DOI

10.2991/mmebc-16.2016.349How to use a DOI?

Keywords

Non-regular descriptor system, white noise, ARMA innovation model, Wiener state estimator

Abstract

Using the modern time-series analysis method in the time domain, based on the autoregressive moving average (ARMA) innovation model and white noise estimator, non-regular descriptor discrete-time stochastic linear systems are researched. Under assumption 1~3, an asymptotically stable reduced-order Wiener state estimator for descriptor systems is given by using projection and block matrix theories. Non-regular descriptor systems include general descriptor systems in them. And the algorithm is reduced-order. It avoids the solution of the Riccati equations and Diophantine equations. So that it reduces the computational burden, and is suitable for real time applications.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yan Xu
AU  - Guosheng Zhang
PY  - 2016/06
DA  - 2016/06
TI  - Wiener State Estimator for Non-regular Descriptor System
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 1717
EP  - 1722
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.349
DO  - 10.2991/mmebc-16.2016.349
ID  - Xu2016/06
ER  -