Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1705102 | Applied Mathematical Modelling | 2012 | 10 Pages |
Abstract
This paper presents a new parameter and state estimation algorithm for single-input single-output systems based on canonical state space models from the given input–output data. Difficulties of identification for state space models lie in that there exist unknown noise terms in the formation vector and unknown state variables. By means of the hierarchical identification principle, those noise terms in the information vector are replaced with the estimated residuals and a new least squares algorithm is proposed for parameter estimation and the system states are computed by using the estimated parameters. Finally, an example is provided.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Linfan Zhuang, Feng Pan, Feng Ding,