The Development of Neural Network Based System Identification ...

2.4 AUTOMATIC FLIGHT CONTROL SYSTEM 45
altitude. Simulation results confirmed that the proposed controller scheme is able to
provide a reasonable tracking performance.
2.4.2 Neural Network Based Model Predictive Control
Model Predictive Control (MPC) or also referred as the receding horizon control
(RHC) is an advanced control technique that relies on prediction from a process model,
optimisation and receding horizon implementation. In more detailed definition of MPC,
Mayne et al. [2000] suggest that the MPC is a form of feedback controller in which
N sequence of control actions are obtained by minimising a finite horizon, open-loop
optimisation problem over a prediction horizon of P steps (k, k + 1, k + 2, · · · , k + P ).
The solution of the optimisation process is done at each sampling instant subject to hard
constraints on controls and states, using the current state of the plant as the initial state.
The results of the optimisation process produce N optimal control sequences where only
the first control action from this sequence is implemented to the dynamic plant and
the process is repeated. The general concept of MPC algorithm is best described in
Figure 2.12 which illustrates the behaviour of control input and system output changes
in the MPC framework. In this figure, y k indicates the output or state measurement,
ŷ k+P is the predicted output from the internal MPC model over the prediction horizon,
u k+N−1 is the predicted control input and N denotes the finite future horizon steps.
The MPC technique has been widely used in the petrochemical industry and currently,
the MPC usage has been extended for automotive and aeronautical application. The
MPC ability to handle constraints on the manipulated inputs has been identified as one
of primary reasons for the implementation successes of MPC in industrial applications.
The MPC algorithms are also more flexible in terms of objective function formulation,
time delays and multi-variable process control handling, which subsequently attracts
more attention for their implementation in industries. Several application examples
of MPC in automotive or aeronautical applications can be found in Castillo et al.
[2007], Dalamagkidis et al. [2010], Joelianto et al. [2011] and Dahunsi and Pedro [2010].
Several survey papers have been published to overview the MPC histories, theoretical
development, practical application and critical issues regarding MPC implementation