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• W2782745581 abstract "The continuous-time subspace identification using state-variable filtering has been investigated for a long time. Due to the simple orthogonal basis functions that were adopted by the existing methods, the identification performance is quite sensitive to the selection of the system-dynamic parameter associated with an orthogonal basis. To cope with this problem, a subspace identification method using generalized orthonormal (Takenaka-Malmquist) basis functions is developed, which has the potential to perform better than the existing state-variable filtering methods since the adopted Takenaka-Malmquist basis has more degree of freedom in selecting the system-dynamic parameters. As a price for the flexibility of the generalized orthonormal bases, the transformed state-space model is time-varying or parameter-varying which cannot be identified using traditional subspace identification methods. To this end, a new subspace identification algorithm is developed by exploiting the structural properties of the time-variant system matrices, which is then validated by numerical simulations." @default.
• W2782745581 created "2018-01-26" @default.
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• W2782745581 date "2017-12-01" @default.
• W2782745581 modified "2023-09-25" @default.
• W2782745581 title "Subspace identification of continuous-time models using generalized orthonormal bases" @default.
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• W2782745581 doi "https://doi.org/10.1109/cdc.2017.8264440" @default.
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