The Development of Neural Network Based System Identification ...

Chapter 6
NEURAL NETWORK BASED PREDICTIVE
CONTROL SYSTEM
6.1 INTRODUCTION
The fundamental concepts of NN based model predictive control (MPC) are presented in
this chapter. For the design of an adaptive flight controller for the helicopter UAS, the
non-linear models identified from the NN based system identification are employed. The
NN based MPC is a NN based control algorithm classified under indirect NN control
system design [Agarwal, 1997, Norgaard, 2000]. The design of this type of controller
always depends on the availability of the dynamic model of the system. However, the
controller design using this approach often follows the conventional controller design
while the NN models obtained in advanced are merely used as an aid to the controller
development. Some examples of conventional control methods used in indirect NN
control designs include approximate pole placement, minimum variance, predictive
control, and non-linear predictive control design have been introduced and discussed in
detailed in Norgaard [2000].
In this chapter, the Neural Network based Approximate Predictive Controller
(NNAPC) is discussed. It is designed and developed using the NN model identified from
off-line or on-line system identification algorithms. The rest of the chapter is organised
as follows. Section 6.2 describes the motivation to use such an approach and the basics
of NNAPC design. Section 6.3 provides the details of the instantaneous linearisation
concept to extract the necessary linear model for the NNAPC’s prediction process. The
formulation of the state space model from the linear ARX transfer function model and