The Development of Neural Network Based System Identification ...

46 CHAPTER 2 LITERATURE REVIEW
Past
Future
Reference
Output
Predicted Output
Predicted Input
y k-1
y k+2
y k+1
y k
ŷ k+1 ŷ
ŷ k+2
k
u k+1
u k+2
u k+3
u k+N-1
ŷ k+P
y k-2
k k+1 k+2 time
Prediction Horizon, P
Control Horizon, N
Figure 2.12
The implementation concept of model predictive control (MPC).
such as modelling, identification, robustness, state estimation, stability and optimality
[Camacho and Bordons, 2007, Mayne et al., 2000, Rawlings, 2000, Bequette, 2007].
The primary objective of the MPC is to formulate a trajectory of future manipulated
inputs to optimise the future behaviour of the dynamic system. The optimisation process
is done in a fixed size moving horizon window which differs from other traditional control
techniques that use pre-calculated control law and do not explicitly consider the future
implication of the current control action [Mayne et al., 2000]. In order to bring the future
or predicted system output as close as possible to the reference signal, the optimisation
of the manipulated control input needs to be implemented recursively using the current
state measurement of the plant. The future behaviour of the plant is predicted using a
process model obtained either through mathematical modelling or system identification
approach.
The linear MPC design generally utilises linear models such as the finite impulse
response (FIR)/step response models, transfer function model and state space model
[Rossiter, 2003, Wang, 2009b]. Wang and Young [2006] reports that the application
of FIR models is limited to stable plant and requires a large model order, whereas the