Identification of continuous-time MIMO state space models from sampled data, in the presence of process and measurement noise
Identification of continuous-time MIMO state space models from sampled data, in the presence of process and measurement noise
复制标题
在存在过程和测量噪声的情况下，从采样数据中识别连续时间 MIMO 状态空间模型
DOI：
10.1109/cdc.1996.572741
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发表时间：
1996
期刊：
影响因子：
--
通讯作者：
Rolf Johansson
中科院分区：
文献类型：
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作者：
B. Haverkamp;C. T. Chou;M. Verhaegen;Rolf Johansson
本文提出了一种连续时间子空间模型辨识方法，用于MIMO状态空间模型的辨识。假设测量的输入和输出信号是在规则间隔的采样时刻测量的。测量和过程噪声的存在被认为是。所提出的方法给出了系统矩阵的有偏估计，但我们将展示如何通过适当选择用于仪器的滞后来最小化这种偏差。最后，通过对飞机动力学参数的辨识，验证了该方法的适用性。
This paper demonstrates the use of a continuous-time subspace model identification method, in the identification of MIMO state-space models. The measured input and output signals are assumed to be measured at regularly spaced sampling instances. The presence of both measurement and process noise is considered. The proposed method gives a biased estimate of the system matrices, but we shall show how to minimise this bias by a proper choice of the lag used for the instruments. Finally, the applicability of the method is demonstrated in the identification of aircraft dynamics.