Identification of non-linear aeroelastic models from experimental data: methods and applications

Abstract eng:
In this paper we consider the problem of parameter estimation from experimental data for aeroelastic models. The work is inspired and motivated by applications dealing with rotorcraft vehicles. We describe two alternative parameter estimation classes of methods in the time domain, namely the recursive filtering and the batch optimization methods. Both classes of methods are formulated so as to be applicable to complex first-principle vehicle models. In the recursive approach, we formulate a novel version of the Extended Kalman Filter which specifically accounts for the presence of unobservable states in the model. An important highlight of the proposed approach is that it does not require a reduced-order model of the system. In the case of the batch optimization methods, we present a formulation based on a new single-multiple shooting approach specifically designed for vehicle models with slow and fast solution components. The document is concluded by a preliminary assessment of the performance of the proposed procedures with the help of applications regarding manned and unmanned rotorcraft vehicles, using both data obtained by simulation and gathered during flight testing.

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