The Development of Neural Network Based System Identification ...

48 CHAPTER 2 LITERATURE REVIEW
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Objective Criterion, J
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Manipulated Input, u
Figure 2.13 The illustration of non-convex optimisation with multiple local minima. The optimisation
solution became trapped in local minima after prediction horizon increased greater than 5. Figure
adapted from [Bequette, 2007].
are then fine tuned appropriately for each linear model. The proposed strategy is similar
to the strategy employed in a gain scheduling controller. Joelianto et al. [2011] reports
that the MPC controllers are capable of handling the transition between linear models
with exceptional control performance. However, the suggested controllers can result
in a control signal that jumps to different values at each model transition. This effect
needs to be reduced in practice as the sudden increase in the control action can result
in dangerous sudden movement of the helicopter.
The neural network based ARX (NNARX) model is a popular non-linear model
structure that can be used with the MPC algorithm [Norgaard, 2000, Soloway and
Haley, 1996, Norgaard et al., 1996, Sadhana Chidrawar and Waghmare, 2009]. In Samal
[2009], a NN based MPC was developed for an unmanned helicopter UAS that takes
into account the constraints and non-linearity of the helicopter dynamics. The NN
model used in the MPC formulation and system identification is based on NNARX
architecture. The optimisation of the NN based MPC approach is solved using the
non-linear optimisation technique. The performance of the proposed controllers is
validated in simulation and real flight tests. Numerical simulation results show the
robustness of the proposed controller under the effects of actuator and sensor delays,
measurement noises, wind gusts and possible parameter uncertainties. Furthermore,