New insight:
In this project, we used the parameter identification approach instead of using the theoretical model. So we use the System Identification Toolbox 7.0 in Matlab R2007a as a tool for that kind of approach. The first step is to prepare the flight data for identification. In this step, I removed the data means by using ‘detrend’. Then, the detrended data was divided into two parts. The first is for the estimation data and the second one is for the validation data. Read the rest of this entry ?
This week I studied some model structure for system identification process. And I found a very helpful source in internet to this subject (thanks to Mr. Google). The tutorial is from National Instruments. These are some citation of the tutorial.
The ARX model is the simplest model incorporating the stimulus signal. The estimation of the ARX model is the most efficient of the polynomial estimation methods because it is the result of solving linear regression equations in analytic form. Moreover, the solution is unique. In other words, the solution always satisfies the global minimum of the loss function. The ARX model therefore is preferable, especially when the model order is high. Read the rest of this entry ?
To assess the data and the degree of difficulty in identifying a model, you first estimate the simplest, discrete-time model to get a relationship between u(t) and y(t) — the ARX model. This black-box approach does not require you to model the physics of your system. The ARX model is a linear difference equation that relates the input u(t) to the output y(t) as follows:
