The Development of Neural Network Based System Identification ...

xviii
LIST OF FIGURES
2.13 The illustration of non-convex optimisation with multiple local minima.
The optimisation solution became trapped in local minima after prediction
horizon increased greater than 5. Figure adapted from [Bequette, 2007]. 48
2.14 Different configuration of NN architectures that represent time varying
linear model [Kuure-Kinsey et al., 2006a, Kuure-Kinsey and Bequette,
2008]: (a) Feed-forward NN architecture (b) Recurrent NN architecture 50
3.1 The TREX600 helicopter used in this research project with instrumentation
equipment fitted between the fuselage and the landing gear. 56
3.2 Side frame system and components attachment in TREX600 main structure. 58
3.3 The stabilising effect of the stabiliser bar. Figure adapted from Kim and
Tilbury [2004]. 59
3.4 The basic cyclic and collective pitch mechanism in the helicopter control
system. β indicates the stabiliser bar flapping with respect to the body
coordinate frame attached to the rotor hub. Symbol ‘◦’ indicates ball
joints and fixed joints are shown as ‘•’. Figure adapted from Kim and
Tilbury [2004]. 60
3.5 Spektrum AR7000 receiver and actuator (servo) connections. 62
3.6 The gyro automatically corrects changes in the helicopter tail trim by
crosswind. 63
3.7 Translational functionality of the test stand. 64
3.8 The roll, pitch and yaw motion arrangement in test stand. (a) Test stand
view from the side; and (b) Test stand view from the back. 66
3.9 The servo locking mechanism of the rotational motion (roll, pitch and
yaw) of the test rig. 67
3.10 The overall architecture for the developed automatic flight controller
system. 68
3.11 Helicopter avionics and sensors on the test stand. 69
3.12 The close up view of the linear UniMeasure LX-PA-30 position transducer. 71