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In this work, a novel data-based stochastic global identification frameworkis introduced for air vehicles operating under varying flight states anduncertainty. In this context, the term global refers to the identification of amodel that is capable of representing the system dynamics under any admissibleflight state based on data recorded from sample states. The proposed frameworkis based on stochastic time-series models for representing the system dynamicsand aeroelastic response under multiple flight states, with each statecharacterized by several variables, such as the airspeed and angle of attack,forming a flight state vector. The method's cornerstone lies in the new classof Vector-dependent Functionally Pooled (VFP) models which allow the explicitanalytical inclusion of the flight state vector into the model parameters and,hence, system dynamics. The experimental evaluation is based on a prototypebio-inspired self-sensing composite wing that is subjected to a series of windtunnel experiments. Distributed micro-sensors in the form of stretchable sensornetworks are embedded in the composite layup of the wing to provide the sensingcapabilities. Data collected from piezoelectric sensors are employed for theidentification of a stochastic global VFP model. The estimated VFP modelparameters constitute two-dimensional functions of the flight state vectordefined by the airspeed and angle of attack. The identified model is able tosuccessfully represent the aeroelastic response of the wing under theadmissible flight states via a minimum number of estimated parameters comparedto standard identification approaches. The obtained results demonstrate thehigh accuracy and effectiveness of the proposed global identificationframework, thus constituting a first step towards the next generation offly-by-feel aerospace vehicles with state awareness capabilities.

A Stochastic Global Identification Framework for Aerospace Vehicles Operating Under Varying Flight States