The Development of Neural Network Based System Identification ...

7.3 EXPERIMENTAL RESULTS 193
Flight Controller
NNAPC
Augmented
Yaw
Dynamic
IMU
PI
Controller
Yaw
Dynamic
Yaw Rate
Gyro
Commercial Yaw Rate
Feedback Controller
Figure 7.5 The block diagram showing the links between the on-board flight controller, the bare yaw
dynamic and the components of augmented system in the yaw channel: the yaw rate gyro and the PI
controller. G T indicates the actuator gain for the actuator that controls the tail rotor pitch while K ψ
denotes the controller gain for yaw angle compensation.
from spinning out of control in the yaw axis, a saturation limit is introduced in the
NNAPC controller calculation that bounds the calculated control action value between
−0.2 → 0.2.
Table 7.4 summaries the controller performance for this comparison using the MSE,
settling time, overshoot and rise time performance indicators. The result shows that the
controller with N p = 10 and N p = 12 produces better response compared with N p = 8.
The selection of prediction horizon length that is lower than N p = 8 would result in an
unstable closed loop response. The prediction horizon N p parameter needs to be selected
long enough to ensure that the closed loop system is stable and satisfies the control
performance criteria [Maciejowski, 2002]. However, it is often impossible to choose a
prediction horizon that predicts too long into the future as the solution can increase
the computation time of the NNAPC controller. Moreover, the NNAPC optimisation is
based on the prediction from the extracted linear models which are only valid in the
linearisation window. Thus, the prediction from these linear models is not sufficiently
accurate for prediction in the far future. Several alternative methods that can be used
to approximate the N p value are based on the rise time or dead time information of the