New Closed-Loop Identification Approach Based on Output Over-Sampling Scheme
New Closed-Loop Identification Approach Based on Output Over-Sampling Scheme
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DOI：
10.3182/20120711-3-be-2027.00265
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发表时间：
2012-07
期刊：
影响因子：
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通讯作者：
Lianming Sun;Yucai Zhu
中科院分区：
文献类型：
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作者：
Lianming Sun;Yucai Zhu
摘要利用输出过采样数据的循环平稳特性，提出了一种新的直接闭环辨识算法。结果表明，在输出过采样方案中，即使在测试输入中有较少的激励，也可以直接从输入和输出数据中识别对象模型。然而，传统的直接算法的数值优化通常依赖于噪声过程的初始值和估计，而噪声模型的估计精度易受传递函数的极点和零点的影响。本文分析了输出过采样方案中采样数据的固有循环平稳特性和相关的子空间特性。结果表明，这些性质可以应用于辨识，并可以减少数值优化中对噪声模型估计和初值敏感性的影响。仿真结果表明，该算法在直接闭环辨识中能显著提高辨识性能。
Abstract A new identification algorithm is investigated for direct closed-loop identification by using the cyclostationarity of output over-sampled data. It has been shown that the plant model can be directly identified from the input and output data in the output over-sampling scheme even less excitation is available in the test input. However, the numerical optimization in conventional direct algorithms ordinarily depends on the initial values and estimation of noise process, whereas the estimation accuracy of the noise model is fragile to the poles and zeros of its transfer function. The properties of instinct cyclostationarity and the associated subspace characteristics of the sampled data in the output over-sampling scheme are analyzed in the paper. It illustrates that these properties can be applied for identification, and can reduce the influence of sensitivity to the noise model estimation and initial values in the numerical optimization. The simulation examples illustrate that the proposed algorithm can significantly improve the identification performance in direct closed-loop identification.