期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 98, 期 -, 页码 684-701出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2017.05.013
关键词
Time-varying structures; Time-dependent autoregressive moving average; Kernel recursive extended least squares; Output-only identification; Modal parameter estimation
资金
- China Scholarship Council
- National Natural Science Foundation of China [11402022]
- Fund for Scientific Research - Flanders (F.W.O.)
- Research Fund KU Leuven
The problem of parametric output-only identification of time-varying structures in a recursive manner is considered. A kernelized time-dependent autoregressive moving average (TARMA) model is proposed by expanding the time-varying model parameters onto the basis set of kernel functions in a reproducing kernel Hilbert space. An exponentially weighted kernel recursive extended least squares TARMA identification scheme is proposed, and a sliding-window technique is subsequently applied to fix the computational complexity for each consecutive update, allowing the method to operate online in time varying environments. The proposed sliding-window exponentially weighted kernel recursive extended least squares TARMA method is employed for the identification of a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudo-linear regression TARMA method via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics. Furthermore, the comparisons demonstrate the superior achievable accuracy, lower computational complexity and enhanced online identification capability of the proposed kernel recursive extended least squares TARMA approach. (C) 2017 Elsevier Ltd. All rights reserved.
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