The Development of Neural Network Based System Identification ...

206 CHAPTER 7 FLIGHT CONTROL SYSTEM DESIGN: RESULTS AND DISCUSSION
7.4 SUMMARY
The flight test results for the unmanned helicopter system controlled by multiple NNAPC
controllers are presented in this chapter. The automatic hovering flight controllers are
designed according to the TITO architecture which result in three NNAPC controllers
i.e. the coupled roll-pitch controller, the yaw rate controller and the altitude speed
controller. Several flight tests have been conducted in the early parts of controller
development to identified the control tuning parameters such as r w , N p , K Ψ and K w
that would produced satisfactory control performance. The HMLP network presented
in the Subsection 4.2.2 is used in the NNAPC controller prediction due the network
capability to model the flight dynamics with fewer weight connections compared with
the standard MLP network. The control tuning results show that tuning parameters
such as r w = 1.5, N p = 10, K Ψ = 1.0 and K w = 0.75 produce the best flight control
response compared with other tuning setting. Using the tuning parameters identified
from the control tuning tests, a full 4 DOF NNAPC controller is successfully realised
in the hovering flight condition with good compensation performance. In the hovering
flight test, the unmanned helicopter is commanded to track a step response in altitude
for a duration of 19.8 s while stabilising the helicopter in roll, pitch and yaw channels.
After the successful implementation of the 4 DOF flight controllers, the NNAPC
controllers are further tested under the physical parameter variations that involve
changes in the collective pitch curve setting, throttle curve setting and the test rig’s
counterbalance weight. The changes in the counterbalance weight can be equated to the
changes in the unmanned helicopter payload during flight. Similar to the approach taken
by Samal [2009], the changes in both collective and throttle curves of the helicopter
blades are used as the test reference. In real flight condition, the total lift and drag
forces acting on the helicopter vary during different flight and environment conditions.
This can be treated similarly in the indoor test by setting changes in the collective pitch
and throttle setting during flight. The performance of the NNAPC-Offline controller
is then compared with the NNAPC-Online controller under parameter variation tests.
The off-line NN model and the initial model for recursive HMLP are obtained using the