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Research Papers

A New Efficient System Identification Method for Nonlinear Multiple Degree-of-Freedom Structural Dynamic Systems

[+] Author and Article Information
Yuzhu Guo

Department of Automatic Control
and Systems Engineering,
University of Sheffield,
Mappin Street,
Sheffield S1 3JD, UK
e-mail: yuzhu.guo@shef.ac.uk

L. Z. Guo

Department of Automatic Control
and Systems Engineering,
University of Sheffield,
Mappin Street,
Sheffield S1 3JD, UK
e-mail: l.guo@shef.ac.uk

S. A. Billings

Department of Automatic Control
and Systems Engineering,
University of Sheffield,
Mappin Street,
Sheffield S1 3JD, UK
e-mail: s.billings@shef.ac.uk

Z. Q. Lang

Department of Automatic Control
and Systems Engineering,
University of Sheffield,
Mappin Street,
Sheffield S1 3JD, UK
e-mail: z.lang @ sheffield.ac.uk

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received September 26, 2013; final manuscript received August 18, 2015; published online October 1, 2015. Assoc. Editor: Corina Sandu.

J. Comput. Nonlinear Dynam 11(2), 021012 (Oct 01, 2015) (8 pages) Paper No: CND-13-1229; doi: 10.1115/1.4031488 History: Received September 26, 2013; Revised August 18, 2015

A new efficient system identification method is introduced to determine the model structure and parameter estimates of an unknown structural system to produce a multi-input multi-output (MIMO) model. No a priori knowledge is assumed regarding the nonlinearities. When the system involves lumped masses, all the information about the locations, types, and coefficients of both linear and nonlinear links which connect the lumped masses in the system can be determined in one efficient procedure. The new algorithm yields a parsimonious continuous time model which is useful for the interpretation of the system in practical applications. An illustrative example demonstrates the efficiency of the new method.

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Figures

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Fig. 1

An example of system with two nonlinear links

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Fig. 2

An MDOF structural system

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Fig. 3

Approximation of the nonlinearity nl3 between m2 and m4 : (a) ninth-order approximation of cube-root nonlinearities in subsystem 2 and 4 and (b) approximation of the cube-root nonlinearity by different orders of polynomials

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Fig. 4

Responses of the original system and identified model to a harmonic input applied on m3 :—output of the real system and – output of the identified model

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Fig. 5

Phase portraits of the response of the original system and the model predicted output of the identified model: (a) original system and (b) identified model

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