Grey-box state-space identification of nonlinear mechanical vibrations

Abstract

The present paper deals with the identification of nonlinear mechanical vibrations. A grey-box, or semi-physical, nonlinear state-space representation is introduced, expressing the nonlinear basis functions using a limited number of measured output variables. This representation assumes that the observed nonlinearities are localised in physical space, which is a generic case in mechanics. A two-step identification procedure is derived for the grey-box model parameters, integrating nonlinear subspace initialisation and weighted least-squares optimisation. The complete procedure is applied to an electrical circuit mimicking the behaviour of a single-input, single-output (SISO) nonlinear mechanical system and to a single-input, multiple-output (SIMO) geometrically nonlinear beam structure.

References in corpus (3)

• Suppression of Limit Cycle Oscillations using the Nonlinear Tuned Vibration Absorber
• Identification of Nonlinear Normal Modes of Engineering Structures under Broadband Forcing
• Recursive Discrete-Time Models for Continuous-Time Systems Under Band-Limited Assumptions

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