The Development of Neural Network Based System Identification ...

156 CHAPTER 6 NEURAL NETWORK BASED PREDICTIVE CONTROL SYSTEM
addition of an integrator term into the state space model are presented in Section 6.4
and 6.5. Section 6.6 and 6.7 describe the model prediction process using the state space
model and optimisation routine for NNAPC design. In Section 6.8, the NNAPC design
with constraints is discussed in detail. The operational constraints in the optimisation
step are introduced into NNAPC design to improve the performance of the control
system. Then, the chapter presents the basic control architectures of helicopter UAS
and NNAPC algorithm implementation in Section 6.9. The chapter is summarised in
Section 6.10.
6.2 NN BASED APPROXIMATE PREDICTIVE CONTROL
PRINCIPLES
The main objective of the MPC implementation is to compute a trajectory of future
manipulated variable u to optimise the future plant output ŷ over a specified time horizon
[Camacho and Bordons, 2004, Rawlings, 2000]. Figure 6.1(a) shows the basic architecture
of the NN based MPC which uses prediction from a NN model to generate future plant
output. In this approach, a process model such as NN provides a prediction of the future
plant response over the specified horizon before being fed into an optimisation process.
The optimisation process is carried out within a limited time horizon by giving the
initial plant dynamics at the start of the process. System operational constraints can
also be incorporated into the optimisation process which improves the control system
performance when the control signals or system states violate the operation constraints.
The optimisation process will be responsible for finding the value of the manipulated
variable u which minimises a specified performance criterion given the current state
measurement and future reference trajectory.
The optimisation procedure using multi-step ahead prediction from the non-linear
NN model often results in demanding computation efforts [Witt et al., 2007]. This is due
to the minimisation of non-convex optimisation cost criterion because of the introduction
of a non-linear model such as NN model [Samal, 2009]. The optimisation problem
becomes a complex non-linear programming problem with no guarantee of reaching
global minimum as the prediction from the model is determined by the non-linear