The Development of Neural Network Based System Identification ...

40 CHAPTER 2 LITERATURE REVIEW
control schemes can be categorised into six main classes as follows [Norgaard, 2000]:
1. Direct inverse Controller
2. Internal model Controller (IMC)
3. Feed-forward controller with inverse model
4. Feedback linearisation controller
5. Optimal controller
6. Non-linear Model Predictive Controller (NMPC)
Samal [2009], Norgaard [2000], Agarwal [1997] suggested that the first five types of
NN controller schemes fall under direct adaptive control class where the NN is used to
update the controller parameters. Whereas, the NMPC is an indirect type of adaptive
controller where the NN model is used to aid the existing conventional MPC controller
to drive the system response towards the desired reference trajectory.
One of the earliest NN methods used to control the non-linear flight dynamics is the
direct inverse controller approach. In this approach, the NN was trained to act as an
inverse of the system. This results in a NN representation that directly maps the sensor
inputs (past output and input measurements) to actuator controls. This representation
was later used as a controller for the system. Mathematically, the inverse model of the
system is described as follows:
u(t) = f −1 [y(t + 1) y(t) · · · y(t − n y + 1) u(t) · · · u(t − n u )] (2.3)
After the NN controller has been trained, the inverse model can be used to control the
system by substituting the output at time t + 1 with the reference set-point r(t + 1)
[Norgaard, 2000]. The inverse model can be trained either using the off-line training or
on-line training methods used in system identification problem. The general principle
of the direct inverse controller is illustrated in Figure 2.9.
In Buskey et al. [2001], a direct inverse type NN controller was designed and
developed for automatic hovering of the JR Ergo RC helicopter. The weights of the