This paper describes an investigative modeling effort through system
identification technique on an unmanned aerial vehicle (UAV). Linear
state-space models corresponding to the longitudinal and lateral dynamics
of a fixed-wing air vehicle are obtained through the implementation of two
well-established identification methods, namely subspace method and
prediction error method (PEM). Although both methods differ in terms of
their fundamental principles, they complement each other very well and
may be applied to the same dynamic system together thus improving the
fidelity of the identified model. The combined subspace-PEM identification
routine proposed in this paper is applied on the Spoonbill UAV system
which is the latest long endurance unmanned aerial vehicle (UAV) system
currently under development by the Remotely Piloted Vehicle and
Microsatellite Laboratory (RMRL), National Cheng Kung University,
Taiwan. Actual flight tests are being carried out to gather necessary
input-output data for system identification. Specifically designed input
flight maneuvers are executed during flight tests in order to yield better
quality data which is crucial in producing good identified models.
Identification results corresponding to longitudinal and lateral dynamics
are presented and discussed in this paper. They show encouraging accuracy and the simplicity of the identification algorithm accentuate the benefits of the proposed identification scheme. Furthermore, the algorithm is applicable to any UAV system as long as the associated air vehicle is of conventional design.