The Development of Neural Network Based System Identification ...

42 CHAPTER 2 LITERATURE REVIEW
Vijaya Kumar et al. [2011]. The theoretical results presented are validated using a
non-linear 6 DOF helicopter simulation undergoing several agile flight manoeuvres. The
NN controllers developed are found to perform well in the presence of gust, sensor
noise and modelling uncertainty. Moreover, the NN controllers are shown to meet the
frequency domain and phase delay requirements of the U.S. Army Aeronautical Design
Standard for rotorcraft handling qualities (ADS-33E-PRF). Although the stability
analysis has been developed for such a controller, no extensive experimental evaluation
has been performed yet to test the performance of the NN controller.
In Suresh and Sundararajan [2012], a feed-forward adaptive NN controller scheme
was proposed for a helicopter performing highly non-linear manoeuvres. The feed-forward
NN control strategy used a NN controller to aid a basic LQR controller as shown in
Figure 2.10. The NN controller is based on the on-line learning RBF network which
used the Lyapunov based rules update to achieve global stability and better tracking
performance. The pre-training and prior selection of the number of hidden neurons
that accurately represent the inverse model are not required for the on-line learning
RBF network. Instead, the appropriate number of neurons is determined recursively
through the use of neurons growing and pruning algorithms. In the growing algorithm,
the neurons of the inverse model are allowed to grow based on the corresponding error
signal thresholds. In order to reduce the complexity of the NN model, the pruning
strategy is incorporated in the algorithm to delete the non-contributing neurons. The
simulation studies carried out in this work used a non-linear 6 DOF helicopter model
similar to BO-105 helicopter. The performance simulation results clearly show the
superior handling qualities of the proposed NN controller at various flight speeds and
operating conditions. The results also indicate that the proposed on-line learning NN
controller was able to adapt to the dynamic changes and provide the necessary tracking
performance in executing highly non-linear manoeuvres such as obstacle clearance and
elliptic manoeuvres.
The NN based adaptive linearisation approach is in principle similar to standard
linearisation controller used in Isidori [1995] and Slotine and Li [1991]. Figure 2.11 shows
the general implementation of NN based feedback linearisation approach. As shown in