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Modular Subspace-Based System Identification From Multi-Setup Measurements
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DOI：10.1109/TAC.2012.2193711.png)
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摘要
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Subspace identification algorithms are efficient for output-only eigenstructure identification of linear MIMO systems. The problem of merging sensor data obtained from moving and non-simultaneously recorded measurement setups under varying excitation is considered. To address the problem of dimension explosion, when retrieving the system matrices of the complete system, a modular and scalable approach is proposed. Adapted to a large class of subspace methods, observability matrices are normalized and merged to retrieve global system matrices.
Keyword:
Blind eigenstructure identification
iterative least-squares
misspecified model order
moving sensors
nonstationary excitation
subspace methods
vibration analysis
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IF:
7
论文数:
1.3W
被引数:
6.7W
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