000013154 001__ 13154
000013154 005__ 20161114160329.0
000013154 04107 $$aeng
000013154 046__ $$k2009-06-22
000013154 100__ $$aBottasso C., L.
000013154 24500 $$aIdentification of non-linear aeroelastic models from experimental data: methods and applications

000013154 24630 $$n2.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000013154 260__ $$bNational Technical University of Athens, 2009
000013154 506__ $$arestricted
000013154 520__ $$2eng$$aIn 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.

000013154 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000013154 653__ $$aParameter Estimation, Optimization, Aero-servo-elasticity, Multibody Dynamics, Rotorcraft Vehicles. Abstract. 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.

000013154 7112_ $$aCOMPDYN 2009 - 2nd International Thematic Conference$$cIsland of Rhodes (GR)$$d2009-06-22 / 2009-06-24$$gCOMPDYN2009
000013154 720__ $$aBottasso C., L.$$iLuraghi, F.$$iMaisano, G.
000013154 8560_ $$ffischerc@itam.cas.cz
000013154 8564_ $$s1007875$$uhttps://invenio.itam.cas.cz/record/13154/files/CD207.pdf$$yOriginal version of the author's contribution as presented on CD, section: Identification methods in structural dynamics - iii - (MS).
000013154 962__ $$r13074
000013154 980__ $$aPAPER