文章基本信息

标题：Stabilized Prediction Error Method for Closed-loop Identification of Unstable Systems
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作者：Ichiro Maruta ; Toshiharu Sugie
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：479-484
DOI：10.1016/j.ifacol.2018.09.191
语种：English
出版社：Elsevier
摘要：AbstractThe prediction error method (PEM) based on output error is a versatile tool for tuning parameters in models from the I/O data of the target systems. However, if the target system is unstable and data is acquired in a closed-loop environment, a naive application of the PEM fails. For this problem, a stabilized version of PEM, where a virtual controller stabilizes the prediction error, is introduced in this paper. For linear models, the performance of the proposed method compares favorably with conventional closed-loop identification methods. While it has the versatility to accommodate a wide range of nonlinear models as long as it can simulate the output of the target system. In this paper, the performance and versatility of the proposed method are demonstrated through analysis based on linear models and numerical examples with nonlinear models.
关键词：KeywordsClosed-loop system identificationStabilized prediction error methodOutput error model