Parameter and state estimation algorithm for single-input single-output linear systems using the canonical state space models

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• 作者：Linfan Zhuang^a ; ^{linfanz@qq.com} ; Feng Pan^a ; ^{pan_feng_63@163.com} ; Feng Ding^a ; ^b ; ^{fding@jiangnan.edu.cn}
• 关键词：State estimation ; Parameter estimation ; Least squares ; State space model ; Recursive identification
• 刊名：Applied Mathematical Modelling
• 出版年：2012
• 期刊代码：6_0307904x
• 类别：cp
• 出版时间：August, 2012
• 卷：36
• 期：8
• 页码：3454-3463
• 文件大小：370 K

摘要

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.