The Development of Neural Network Based System Identification ...

158 CHAPTER 6 NEURAL NETWORK BASED PREDICTIVE CONTROL SYSTEM
As an alternative to overcome this problem, a NN controller can be trained in
off-line mode to mimic a conventional MPC controller. Subsequently this would reduce
the computation and the tuning effort. An alternative configuration of NN based MPC
is given in 6.1(b). In this configuration, the neural network controller is used to learn
the controller input selection process calculated by the MPC optimisation algorithm.
The training process of the NN controller is normally conducted off-line and at the end
of the training process, the MPC optimisation step is replaced completely by the trained
NN controller [Agarwal, 1997, Pottmann and Seborg, 1997]. However, this approach is
impractical to implement as the method requires an MPC controller to be present before
the implementation of the NN controller. Moreover, the NN controller performance
could be compromised due to the nature of the off-line training, where certain segments
of the flight operating range could not be properly represented. Furthermore, this
approach is also unattractive to pursue since the NN controller needs to be retrained
all over again whenever the configuration of MPC controller is modified.
This chapter proposes a solution to the aforementioned drawbacks of NN based
MPC real-time implementation by introducing the application of Neural Network based
Approximate Predictive Control (NNAPC) using linear model prediction from identified
NN model obtained either from off-line or on-line modelling techniques. Figure 6.2
shows the block diagram of NNAPC architecture. The NNAPC uses the linearised
model extracted from the NN model through the principle of instantaneous linearisation
[Norgaard, 2000]. Subsequently, this would make the NNAPC to be less computational
and less demanding compared with other MPC techniques such as NN based MPC
or non-linear model predictive control (NMPC) [Ogunfunmi, 2007]. Furthermore, the
NNAPC design parameters are easy to tune and are found effective for a wide range of
control applications and are suitable for systems with time delay [Norgaard, 2000, Witt
et al., 2007, Lawrynczuk, 2007b]. 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 a linear model from the identified NN model at each sampling time and uses it
to predict the future plant response within the specified time horizon.