The Development of Neural Network Based System Identification ...

8 CHAPTER 1 INTRODUCTION
models [Samal et al., 2009, 2010]. Furthermore, the parallel nature and fast adaptability
of neural networks are well suited for adaptive control design and implementation for
unmanned helicopter systems.
1.3 RESEARCH OBJECTIVES
The goal of this research project is to design an automatic flight control system (AFCS)
for autonomous hovering of a single rotor helicopter UAS platform. The performance
and effectiveness of the proposed adaptive controller is evaluated in hovering flight tests.
The objectives set forth for the research work are listed as follows:
1. Develop the system identification algorithms for modelling the non-linear dynamics
of the helicopter UAS in flight.
2. Develop a suitable controller using the identification algorithm developed for
controlling the hovering manoeuvre.
3. Integrate the helicopter platform with the necessary avionics and experimental
apparatus, test and validate the system identification and flight control methods.
1.4 THESIS CONTRIBUTIONS
The major contributions of the thesis are listed as follows:
1. Identification of helicopter UAS dynamics using NN based system identification
methods.
In this study, the neural network based system identification algorithms are developed
to model the non-linear dynamics of the UAS helicopter under consideration
from the flight test data. Since the helicopter dynamics are non-linear, the neural
network system identification approach using NNARX (Neural Network-Auto
Regressive structure with eXtra inputs) model structure is used to address such a
problem. The NNARX structure is able to infer complex non-linear relationship
between inputs-outputs data sets and demonstrate the ability to adapt to changes