The Development of Neural Network Based System Identification ...

ABSTRACT
This thesis presents the development of self adaptive flight controller for an unmanned
helicopter system under hovering manoeuvre. The neural network (NN) based model
predictive control (MPC) approach is utilised in this work. We use this controller due
to its ability to handle system constraints and the time varying nature of the helicopter
dynamics. The non-linear NN based MPC controller is known to produce slow solution
convergence due to high computation demand in the optimisation process. To solve
this problem, the automatic flight controller system is designed using the NN based
approximate predictive control (NNAPC) approach that relies on extraction of linear
models from the non-linear NN model at each time step. The sequence of control input
is generated using the prediction from the linearised model and the optimisation routine
of MPC subject to the imposed hard constraints. In this project, the optimisation of
the MPC objective criterion is implemented using simple and fast computation of the
Hildreth’s Quadratic Programming (QP) procedure.
The system identification of the helicopter dynamics is typically performed using
the time regression network (NNARX) with the input variables. Their time lags are
fed into a static feed-forward network such as the multi-layered perceptron (MLP)
network. NN based modelling that uses the NNARX structure to represent a dynamical
system usually requires a priori knowledge about the model order of the system. Low
model order assumption generally leads to deterioration of model prediction accuracy.
Furthermore, massive amount of weights in the standard NNARX model can result in
an increased NN training time and limit the application of the NNARX model in a
real-time application. In this thesis, three types of NN architectures are considered to
represent the time regression network: the multi-layered perceptron (MLP), the hybrid