In any model-based control design scheme, it is essential to first obtain a math model of the physical system, or the plant we want to control. The design then exploits that information in certain way to craft a controller for that particular plant, subjecting to user specifications or design constraints. So it is rational to say, regardless of any sophisticated design paradigm or software tool used, that a controller will be as good as the model that represents the actual plant.
There are 2 basic approaches to achieve a math model of a physical system. The first relies on theory. One can start from physics; i.e., form an equation and substitute parameters, either from datasheet or measurement. The second approach uses data captured from the real plant and tries to identify the math model from such information. The latter is generally classified as system identification.
This article introduces some common modeling and identification methods, with emphasis on practical issues. Examples from our past research are provided.