The Development of Neural Network Based System Identification ...

6.10 SUMMARY 181
Labview R○ software from National Instrument. It starts with the initialisation of the
NN and controller parameters. At the start of the iteration, the IMU (rotations) and
I2C (altitude) modules collect data and produce the output measurement vector, y(k).
The HMLP module receives the previous calculated control input u(k) and current
output measurement y(k) and constructs a linearised NMSS model. The NN model
implemented in this algorithm can be obtained either from off-line training or recursive
training. The model prediction module receives this state space model and predicts the
future behaviour of the helicopter dynamics. This prediction is later on used in the
Hildereth’s quadratic programming to optimise the control problem objective function
subject to specified constraints. Finally, the calculated control input is sent to the
FPGA module to be executed. The calculated control input and the current output
measurements are also sent to the ground control station (GCS) for monitoring purposes.
6.10 SUMMARY
In this chapter, the theoretical foundation of the NNAPC algorithm was presented.
This algorithm was proposed in order to overcome the computational limitation of the
non-linear NN based MPC. Different components of the NNAPC algorithm such as: the
principle of instantaneous linearisation of the NN model, formulation of the NMSS and
the augmented state space model, the prediction operation, optimisation and constraints
handling of the MPC algorithm were discussed. In the next chapter, the implementation
results of the proposed system identification method and control algorithm are presented
to validate the feasibility of the methods for autonomous hovering flight.