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A new excitation scheme for closed-loop subspace identification using additional sampling outputs and its extension to instrumental variable method

机译:一种使用附加采样输出进行闭环子空间识别的新激励方案,并将其扩展为仪器变量方法

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摘要

It is known that feedback from outputs to inputs causes non-identifiability in many subspace identification algorithms. In this paper, a new excitation method is proposed for closed-loop subspace identification. By adding an output sampling point within two adjacent control times, a difficult closed loop identification issue is recast into an open loop identification. Uncorrelation between the process inputs and the noises in closed loop is obtained, and the persistently exciting condition is proven to be satisfied based on spectral analysis and innovation state space models so that the consistent estimates with no demand for external exciting signals can be guaranteed. Simultaneously, the additional sampling scheme offers different selections of instrumental variables for the non-error-in-variable case and the error-invariable case in some instrumental variables-based identification algorithms. A simulation is performed to test the identification efficiency of the new scheme. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译:众所周知,从输出到输入的反馈会导致许多子空间识别算法无法识别。提出了一种新的激励方法用于闭环子空间识别。通过在两个相邻的控制时间内添加一个输出采样点,可以将一个困难的闭环识别问题重铸为一个开环识别。获得了过程输入与闭环噪声之间的不相关性,并基于频谱分析和创新状态空间模型证明了持续励磁条件得到满足,从而可以确保对估计值的一致估计而无需外部励磁信号。同时,在某些基于工具变量的识别算法中,附加采样方案针对非可变错误情况和误差不变情况提供了工具变量的不同选择。进行仿真以测试新方案的识别效率。 (C)2018富兰克林研究所。由Elsevier Ltd.出版。保留所有权利。

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