The Development of Neural Network Based System Identification ...

38 CHAPTER 2 LITERATURE REVIEW
The desire for enhanced agile manoeuvres and accurate tracking control requires
the unmanned helicopter system to be operated beyond the range of nominal operating
conditions.
In order to extend the capabilities and operating range of the linear
controllers, the non-linear dynamics of the helicopter UAS can be linearised into sets
of linear models each representing certain operating condition.
The tuning of the
controller gains for each linear condition is done via interpolation schemes using the
gain scheduling approach.
However, such a technique has been reported to suffer
performance degradation when performing large amplitude manoeuvres [Kendoul, 2012,
Mettler, 2003, Valavanis, 2007]. In addition to limitation of linear approaches, the
control problem becomes much more challenging to solve as the helicopter operates
under varying atmospheric disturbance.
Although the gain scheduling approach is widely used in unmanned helicopter
control, the tuning of controller’s gains can also be difficult due to the presence of faults
in the system and non-linearity in the operating conditions [Vijaya Kumar et al., 2011].
This specific problem in the gain scheduling control can be overcome by considering
recursive adaptive control methods. The adaptive controller is categorised under model
based non-linear control where the control parameters are updated at every time
step. This control strategy is implemented to rapidly respond to the varying flight
conditions or component failure scenario. Under the adaptive control approach, the
controllers are divided into two types of control configurations [Samal, 2009, Narendra
and Parthasarathy, 1990]: (1) direct adaptive controllers, and (2) indirect adaptive
controllers. Figure 2.8 shows the basic configuration structure of the direct and indirect
adaptive control system for a dynamic system. In direct adaptive controllers, the control
parameters are updated directly to minimise the tracking error. The calculation of
the controller parameters or gains does not rely on the dynamics system to update
the controller parameters. Whereas in the indirect adaptive control configuration, a
dynamic model is used to predict the response of the dynamic plant. The prediction
from the identification model is assumed to be identical to the actual response and this
information is used to minimise the tracking error of the system.