Another modeling technique considers recursive identification methods. This

considers calculating a model simultaneous to obtaining data. However, this is not

a requirement for this specific project but can be useful in different applications.

Certain applications include having an up to date model in order to consider these

parameters when making decisions about what the system is to do next. This is

typically referred to as an adaptive modeling technique because the input and output

signals are calculated in order to be used as they become available.

 An example of a recursive model which can be used for system identification in

Matlab is the RARMAX model. This uses a recursive technique of an ARMAX model

which considers the noise in its calculations. However, this technique only provides

models for single-input, single-output systems. Similarly, another technique is the

RARX model which estimates parameters recursively of a single-output system.

 Therefore, for this project an initial linear approximation was done using an ARX

technique. The initial step was to design an experiment which consisted of specified

maneuvers such as doublets to the morphing and rudder servos. These were done in

order to consider the roll and yaw rate responses of the system.

 The data is then collected and processed before considering it for modeling. The

data processing included using an algorithm which plotted, filtered and removed the

bias in the data. The filtering was done using a low pass Butterworth filter on all the

parameters and the bias was removed from the parameters by subtracting the mean.

 This processed data is then used in the ARX modeling approximation. The roll

rate and yaw rate responses are then compared to the simulation responses. This is

done for both the morphing and rudder servos. The orders and delays are selected for

the parameter estimation.