文章基本信息

标题：Online Features in the MATLAB ® System Identification Toolbox TM
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作者：Lennart Ljung ; Ahmet Arda Ozdemir ; Rajiv Singh 等
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：700-705
DOI：10.1016/j.ifacol.2018.09.201
语种：English
出版社：Elsevier
摘要：AbstractBecause of the increased demand on fault detection, monitoring and predictive maintenance, online or recursive identification is playing a more important role in systems engineering. In the recent releases of System Identification Toolbox™ for MATLAB®, this has been reflected in a more substantial support for online techniques. This contribution gives an account of these improvements. It covers the addition of nonlinear filtering algorithms, such as the extended Kalman filter, the unscented Kalman filter and particle filters. The traditional recursive estimation techniques for polynomial models have also been enhanced with a more versatile syntax. Several new Simulink®blocks have been developed to support Simulink®models with online estimation.
关键词：KeywordsParameter estimationOnline algorithmsRecursive identificationSimulation