The Development of Neural Network Based System Identification ...

6 CHAPTER 1 INTRODUCTION
the dynamics of the helicopter. However, this method requires a significant amount of
work to estimate model parameters through physical measurements and experiments
[Mettler, 2003]. The method also demands solid theoretical knowledge and experiences
about the rotorcraft flight and has the potential to produce unreliable results unless
performed with extreme care. After obtaining the non-linear model, the unmanned
helicopter dynamics is described using linearised model at various flight conditions such
as hover and forward flight conditions. However, the linearised dynamic models are only
valid within the range of operating conditions and multiple linear models are needed
to extend the flight operating condition outside the linear range [Mettler et al., 2002b,
Kendoul, 2012].
Simplified mathematical models have been developed over the years for helicopter
UAS flight controller design [Shim, 2000, Bisgaard, 2007, Heffley and Mnich, 1988].
However, these models suffer performance degradation due to unmodelled or hard to
model dynamic effects that are not incorporated in the mathematical model itself.
There are numerous effects that are typically omitted from the model such as the
ground effect, servo dynamics, rotor speed variation, sensor lag or actuator kinematic
non-linearities. Simplifying assumptions or omitting the mentioned dynamic effects can
introduce significant errors from the model prediction [Garratt and Anavatti, 2012].
Therefore, a more comprehensive modelling approach is required for the modelling of the
dynamics of the helicopter UAS which fully exploit the capabilities presented by their
complex dynamic behaviour. The neural network approach again can be used as an
alternative method in helicopter dynamic modelling. Fast and simpler development of
the dynamic model using the neural network approach should reduce the cost associated
with the development of large aerodynamic databases [Calise and Rysdyk, 1998].
The neural network approach had been used for mathematical modelling or control
application with success [Paliwal and Kumar, 2009, Calise and Rysdyk, 1998]. However,
the neural network modelling method has several disadvantages such as high computational
resources required for training, slow convergence rate, being prone to over-fitting
[Tu, 1996, Wilamowski, 2011a, Norgaard, 2000]. Furthermore, neural networks are
also known to be a ‘black box’ model and have limited capability to express a causal