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The objective of the present paper is to address the identification of a real-life strongly nonlinear space structure, the EADS-Astrium SmallSat spacecraft. To this end, a new nonlinear subspace identification method formulated in the frequency domain is exploited, referred to as the FNSI method. The frequency response functions of the underlying linear spacecraft and the amplitudes of the nonlinear internal forces are estimated based on a periodic-random data set corrupted by noise. This application is challenging for several reasons, including high modal density, highly non-proportional damping and the non-smooth nature of the nonlinearities.

Nonlinear system identification is a challenging task in view of the complexity and wide variety of nonlinear phenomena. The present paper addresses the identification of a real-life aerospace structure possessing a strongly nonlinear component with multiple mechanical stops. The complete identification procedure, from nonlinearity detection and characterization to parameter estimation, is carried out based upon experimental data. The combined use of various analysis techniques, such as the wavelet transform and the restoring force surface method, brings different perspectives to the dynamics. Specifically, the structure is shown to exhibit particularly interesting nonlinear behaviors, including jumps, modal interactions, force relaxation and chattering during impacts on the mechanical stops.

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Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinear state-space models to identify hysteresis dynamics. This approach is shown to provide a general framework to hysteresis identification, featuring flexibility and parsimony of representation. Nonlinear model terms are constructed as a multivariate polynomial in the state variables, and parameter estimation is performed by minimising weighted least-squares cost functions. Technical issues, including the selection of the model order and the polynomial degree, are discussed, and model validation is achieved in both broadband and sine conditions. The study is carried out numerically by exploiting synthetic data generated via the Bouc-Wen equations.