System Identification of Geometrically Nonlinear Structures Using Reduced-Order Models

Abstract

System identification of engineering structures is an established area in the structural dynamics research community. It is often used to characterise certain physical properties of a structure using the data measured from it. For structures exhibiting nonlinear behaviour, physics-based approaches are used where a form of nonlinearity is synthesised and parameters are estimated using the data, or probabilistic approaches are investigated to tackle the model uncertainty of structures. However, to build reliable models, the estimated parameters from the measurement data must reflect the true underlying physics of the structure. Therefore, Reduced-Order Models (ROMs) can be used as the surrogate models, where the nonlinear parameters of the ROMs are having a meaningful relation with the physical parameters of the system. In this work, we propose nonlinear system identification in the context of using some recently developed ROMs which account for the kinetic energy of unmodelled modes. It is shown how ROMs may be used to represent low-order, accurate models for system identification. Identification of a nonlinear system with strong modal coupling is demonstrated, using simulated data, while the estimated ROM response shows good convergence with that of full order system. Similarly, the estimated parameters match with those of directly computed ROM.