The Development of Neural Network Based System Identification ...

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the MLP network, the prediction performance of both of the NN models are on par
with the prediction quality of the MLP network.
The identification results further indicate that the rGN algorithm is more adaptive
to the changes in dynamic properties, although the generalisation error of repeated
rGN is slightly higher than the off-line LM method. The rGN method is found capable
of producing satisfactory prediction accuracy even though the model structure is not
accurately defined. The recursive method presented here in this work is suitable to model
the UAS helicopter in real time within the control sampling time and computational
resource constraints. Moreover, the implementation of proposed network architectures
such as the HMLP and modified Elman networks is found to improve the learning rate
of NN prediction. These positive findings inspire the implementation of the real time
recursive learning of NN models for the proposed MPC controller. The proposed system
identification and hovering control of the unmanned helicopter system are validated
in a 6 degree of freedom (DOF) safety test rig. The experimental results confirm the
effectiveness and the robustness of the proposed controller under disturbances and
parameter changes of the dynamic system.