I'm reading a book about mathematical modelling of dynamic linear(in theory) systems.
As I know, measure simulation data and create a model of the system, is much better and gives a more exact mathematical model of the system.
My question for you is: Which one is best to focus on: State space model e.g MOESP algorithm or ARX, ARMAX models, which gives transfer functions.
They are both good. But estimate state space is a "new" method in the area of system identification, compared to estimate transfer functions.
My question for you is: What should I choose? Focus on state space model estimation or transfer function estimation?
I like state space models better that transfer functions because they give more information and they are not difficult to use. I can also convert a state space model to a transfer function by using the canonical forms.