The Development of Neural Network Based System Identification ...

194 CHAPTER 7 FLIGHT CONTROL SYSTEM DESIGN: RESULTS AND DISCUSSION
dynamic system [Shridhar and Cooper, 1997, Clarke et al., 1987, Garriga and Soroush,
2010]. However, the empirical tuning approach is the most preferred method since the
tuning is based on the actual control performance which gives a better assignment of
N p value compared with the approximation method.
Table 7.4
The control performance comparison with various N p values.
Tuning parameter MSE Settling Time (s) Overshoot (%) Rise Time (s)
θ
φ
r
N p = 8 0.40 1.78 39.20 0.26
N p = 10 0.16 0.84 17.05 0.25
N p = 12 0.06 0.54 4.25 0.34
N p = 8 0.46 n/a n/a n/a
N p = 10 0.12 n/a n/a n/a
N p = 12 0.05 n/a n/a n/a
N p = 8 6.51 n/a n/a n/a
N p = 10 5.50 n/a n/a n/a
N p = 12 7.12 n/a n/a n/a
The tuning results for the yaw angle control are given in Figure 7.6 and Table
7.5. The yaw angle control is achieved by giving r ref = K ψ (ψ ref − ψ m ) as a reference
trajectory to the NNAPC yaw rate controller. The variables ψ ref and ψ m denote the
reference and the measured yaw angle respectively. In this test, the automatic controller
actions are deactivated for the roll and pitch channels while allowing only the automatic
yaw rate controller to compensate for the trajectory error in the yaw channel. The
servomechanisms are also locked for the pitch and roll channels. The yaw controller
performance is tuned with r w = 1.5 and N p = 10 during this test. The result obtained
suggests that the gain value of K ψ = 0.5 gives the lowest rise time with high MSE value,
which indicates a high steady state error in the system response. The yaw controller
gives better compensation performance and faster response as the gain value is increased
to K ψ = 1.
Table 7.5
The control performance comparison with various K ψ values.
Tuning parameter MSE Settling Time (s) Overshoot (%) Rise Time (s)
ψ
K ψ = 0.5 11.20 3.73 1.46 5.02
K ψ = 0.75 8.92 2.48 1.61 2.16
K ψ = 1.0 1.86 1.83 1.31 1.48