The Development of Neural Network Based System Identification ...

44 CHAPTER 2 LITERATURE REVIEW
Reference
Model
x m (t)
-
+
+
Adaptation
for NN f
e(t)
Adaptation
for NN g
NN f
NN g
r(t)
+
-
+ X
-
Plant
x(t)
K
Figure 2.11 The general schematic diagram of a NN based feedback linearisation approach. Figure
adapted from [Hagan and Demuth, 1999].
The flight experiment results suggest the effectiveness of the approach to track the
reference commands properly, and be able to compensate for the undesirable effects of
the un-modelled and time varying dynamics of the helicopter UAV.
Generally, the NN based optimal control approach involves designing a controller
according to a criterion where the trajectory tracking is minimised while incorporating
penalty on the magnitude of the control input. In this approach, the NN model is
used to provide the control inputs that minimises this criterion. In Nodland et al.
[2012], an optimal controller design was proposed for tracking control of an unmanned
helicopter system using an adaptive critic NN framework. In this approach, an online
approximator based dynamic controller was used to learn the continuous time
Hamilton-Jacobian-Bellman (HJB) criterion equation and formulate an optimal control
input that minimised the HJB criterion forward in time. The optimal controller in this
approach consists of a single NN model which is tuned on-line using a novel weight
updating law while maintaining the closed loop system stability. The stability analysis
and performance validation of the proposed controller are done in Simulink R○ simulation
for various flight manoeuvres such as automatic take-off, landing and hovering at certain