The Development of Neural Network Based System Identification ...

1.4 THESIS CONTRIBUTIONS 9
in an operating condition. Previous research work on system identification of
unmanned helicopter system usually neglect proper selection of model structure
and follow a trial and error approach which leads to improper selection of neural
network structure. In order to optimise the network structure selection that lead
to better generalisation and prediction quality, the neural network model structure
in this study was carefully selected using the proposed Lipschitz coefficient and
model validity tests method.
Three types of NN architectures are used to model the dynamics of the helicopter;
namely the MLP, HMLP and the modified Elman network. The HMLP and
modified Elman network are proposed in our work to reduce the total number
of weights used in the MLP network. If the NN training is conducted using
a recursive type training method, the reduced number of NN weights should
reduce the amount of computation needed to train the NN model, which makes
real-time system identification possible. Although the total number of weights for
both HMLP and modified Elman network are lower than the MLP network, the
prediction performance of both models are on par with the prediction quality of
MLP network.
2. The development of recursive type system identification algorithm for on-line
modelling of helicopter dynamics.
The predicted response from the off-line neural network model is found suitable for
modelling the UAS helicopter dynamics correctly. However, the model identified
through the off-line modelling has several drawbacks. The approach has difficulties
in representing the entire flight operation very well because of the time varying
nature of helicopter flight dynamics. The recursive type training such as the
recursive Gauss-Newton (rGN) method is adopted in this study to overcome
such a problem.
The recursive method presented in this work is suitable to
model the UAS helicopter in real time within the control sampling time and
computational resource constraints. Satisfactory prediction quality is achieved
with the rGN training method even with incorrect model structure assignment.
The generalisation and adaptability performance of the model can be further