论文成果
Identification of non-uniformly periodically sampled-data systems based on auxiliary model and singular value decomposition
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- 论文类型：会议论文
- 发表时间：2018-06-09
- 文献类型：A
- 页面范围：617-622
- 摘要：The authors state the non-uniformly periodically sampling pattern and derive the state-space models of non-uniformly periodically sampled-data systems (NUPSS), and further obtains the corresponding transfer function models. The identification difficulties are that there exist unknown inner variables and unmeasurable noise terms in the information vectors. By means of the auxiliary model method, a recursive least squares algorithm using singular value decomposition (SVD) is presented to confirm the model of NUPSS. The purpose of using SVD is to reduce the computational load of the algorithm and to guarantee the stability of the algorithm using singular value decomposition. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed identification method. © 2018 IEEE.
