  In this work, a novel data-based stochastic global identification framework
is introduced for air vehicles operating under varying flight states and
uncertainty. In this context, the term global refers to the identification of a
model that is capable of representing the system dynamics under any admissible
flight state based on data recorded from sample states. The proposed framework
is based on stochastic time-series models for representing the system dynamics
and aeroelastic response under multiple flight states, with each state
characterized by several variables, such as the airspeed and angle of attack,
forming a flight state vector. The method's cornerstone lies in the new class
of Vector-dependent Functionally Pooled (VFP) models which allow the explicit
analytical inclusion of the flight state vector into the model parameters and,
hence, system dynamics. The experimental evaluation is based on a prototype
bio-inspired self-sensing composite wing that is subjected to a series of wind
tunnel experiments. Distributed micro-sensors in the form of stretchable sensor
networks are embedded in the composite layup of the wing to provide the sensing
capabilities. Data collected from piezoelectric sensors are employed for the
identification of a stochastic global VFP model. The estimated VFP model
parameters constitute two-dimensional functions of the flight state vector
defined by the airspeed and angle of attack. The identified model is able to
successfully represent the aeroelastic response of the wing under the
admissible flight states via a minimum number of estimated parameters compared
to standard identification approaches. The obtained results demonstrate the
high accuracy and effectiveness of the proposed global identification
framework, thus constituting a first step towards the next generation of
fly-by-feel aerospace vehicles with state awareness capabilities.

Systems and Control