The Development of Neural Network Based System Identification ...

10 CHAPTER 1 INTRODUCTION
improved by proper network structure selection. This can be obtained with the
aid of the k-fold cross validation method. Furthermore, the implementation of the
proposed network architectures, namely the HMLP and modified Elman networks,
is found to improve the learning rate of NN prediction; and this facilitates the
real-time implementation of the NN based system identification.
3. The development of real-time Neural Network based Approximate Predictive
Control (APC) scheme with constraints.
The Neural Network based Approximate Predictive Controller (NNAPC) is designed
and developed using the NN model identified either from the off-line or
on-line system identification algorithms. The complexity of the proposed controller
algorithm can be handled well enough by the embedded system even with
the implementation of the on-line system identification algorithm. The control
method is proposed to overcome the demanding computation efforts of non-linear
Model Predictive Control (NMPC)’s optimisation procedure. The main difference
between NNAPC with linear MPC and other NMPC methods lies in the prediction
operation of NNAPC. Instead of relying on the prediction from single linear or
non-linear model, the NNAPC controller extracts linear models from the identified
NN model at each sampling time and uses it to predict the future plant response
within the specified time horizon. The proposed NNAPC algorithm is able to
achieve satisfactory tracking control performance with different degrees of control
autonomy. The NNAPC controller is fine tuned for each of the control channels
(roll, pitch, yaw and altitude) and the selected tuning parameters are then used for
the testing of full autonomous hovering control. The NNAPC controller approach
is also tested and proved to be robust enough to handle variation in helicopter
dynamics and demonstrate good disturbance rejection performance to input disturbances.
The flight test results indicate the efficiency of the NNAPC controller
to achieve autonomous hovering.
4. The development of a 6 DOF test rig for safe control flight test of helicopter UAS.
Attempting to control a remote control helicopter is difficult and careful experi-