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Modal parameter identification of flexible spacecraft using the covariance-driven stochastic subspace identification (SSI-COV) method

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Abstract

In this paper, the on-orbit identification of modal parameters for a spacecraft is investigated. Firstly, the coupled dynamic equation of the system is established with the Lagrange method and the stochastic state-space model of the system is obtained. Then, the covariance-driven stochastic subspace identification (SSI-COV) algorithm is adopted to identify the modal parameters of the system. In this algorithm, it just needs the covariance of output data of the system under ambient excitation to construct a Toeplitz matrix, thus the system matrices are obtained by the singular value decomposition on the Toeplitz matrix and the modal parameters of the system can be found from the system matrices. Finally, numerical simulations are carried out to demonstrate the validity of the SSI-COV algorithm. Simulation results indicate that the SSI-COV algorithm is effective in identifying the modal parameters of the spacecraft only using the output data of the system under ambient excitation.

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Acknowledgments

The project was supported by the National Natural Science Foundation of China (Grants 11132001, 11272202, 11472171), the Key Scientific Project of Shanghai Municipal Education Commission (Grant 14ZZ021), the Natural Science Foundation of Shanghai (Grant 14ZR1421000), and the Special Fund for Talent Development of Minhang District of Shanghai.

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  1. Department of Engineering Mechanics, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yong Xie, Pan Liu & Guo-Ping Cai

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  1. Yong Xie
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  2. Pan Liu
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  3. Guo-Ping Cai
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Correspondence to Guo-Ping Cai.

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Xie, Y., Liu, P. & Cai, GP. Modal parameter identification of flexible spacecraft using the covariance-driven stochastic subspace identification (SSI-COV) method. Acta Mech. Sin. 32, 710–719 (2016). https://doi.org/10.1007/s10409-016-0579-x

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  • DOI: https://doi.org/10.1007/s10409-016-0579-x

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