The Development of Neural Network Based System Identification ...

24 CHAPTER 2 LITERATURE REVIEW
Apart from high fidelity modelling theories from standard full scaled helicopter,
several minimum complexity mathematical models can also be used. Several good
examples of the development of minimum complexity mathematical model are given in
Shim [2000], Bisgaard [2007] and Heffley and Mnich [1988]. Although such approaches
can be employed to build a simulation model, the highly non-linear aerodynamics
interaction between body components and the behaviour of the high order dynamics of
a rotorcraft is usually hard to model using the first principle approach (direct physical
understanding of forces and moments balance of the vehicle) and such an approach can
be inaccurate [Mettler, 2003, Budiyono et al., 2009, Deng et al., 2011]. A significant effort
is necessary to validate the theoretical model and re-evaluate any potential shortcomings
associated with the model.
Most of the works adopting the first principle modelling approach in helicopter
modelling use the developed model for control design without detailed validation against
flight data [Kendoul, 2012, Bisgaard, 2007]. Among the few successful works that
use first principle modelling to address the problem of physical parameter estimation
with experimental validation are Nonami et al. [2010] and Gavrilets et al. [2001]. In
Nonami et al. [2010], the non-linear helicopter dynamics is linearised to obtain two
linear state equations that describe the helicopter’s lateral and longitudinal motion.
The linear analytical model includes the rigid-body dynamics, main rotor dynamics
and aerodynamics, stabiliser bar dynamics and aerodynamics effect from other main
body components. The parameters of the model are determined using direct physical
measurements and manufacturer specifications for different helicopter platforms. For
parameters that are more difficult to obtain such as moment inertia in different axes,
the values was measured roughly and manually retuned to match the flight data. The
linear models have been validated for three different helicopter UAV platforms and
results show good fit with the flight data for operating condition around hovering flight.
Gavrilets et al. [2001] describes 17 states of the non-linear dynamics model of an
acrobatic helicopter UAV which includes states for longitudinal and lateral main rotor
flapping, the rotor speed and an integral of the rotor-speed tracking error in addition
to rigid body dynamics. Flight test experiments were used to estimate several key