The Development of Neural Network Based System Identification ...

12 CHAPTER 1 INTRODUCTION
Chapter 3 presents the overview of the helicopter platform and avionic systems used
for the development of the autonomous helicopter UAS. For flight control testing
purposes, a safety test rig has been developed to constraint the helicopter movement
and avoiding fatal crashes which would arise from possible hardware failures or
programming errors.
Chapter 4 presents the procedures of the off-line system identification of helicopter
dynamics using the neural network approach. Then, the on-line (recursive) training
method is proposed to improve the adaptability of the predicted model. The
prediction of the neural network model employed in this study is further improved
using Hybrid Multi-Layered Perceptron (HMLP) and modified Elman networks
which significantly reduce the number of weights in the network and reduce the
required computation time.
Chapter 5 presents the model selection and validation results from the proposed neural
network based system identification methods. The optimal network structures for
system identification are found using the proposed validation method used in the
previous chapter. The performance of standard Multi-Layered Perceptron (MLP)
is then compared to the HMLP and modified Elman network performance.
Chapter 6 presents the model predictive control methodologies using the dynamic
model identified using the neural network based system identification approach.
It proposes solutions to the drawbacks of neural network based MPC real-time
implementation by introducing the application of Neural Network based Approximate
Predictive Control (NNAPC). The prediction of NNAPC is provided using
linear models extracted from the identified NN model obtained from either off-line
or on-line modelling techniques.
Chapter 7 presents the flight tests implementation and validation results of the proposed
adaptive flight controller. In this chapter, the tuning procedures of the
NNAPC flight controller are presented. The proposed flight controller is designed
in several development stages where the best tuning parameters obtained from