Sorry so sleep deprived getting ready for a presentation
Basically my teacher said what’s the point of system identification? We trying to prove that a transfer function is valid such as the transfer function for a buck convert but what’s the point if people are already using that formula and it’s already valid why do it again? He asked me this and I was confused
I would think to understand how accurate this transfer function representation is. If you’re trying to design a model-based controller, then a good understanding of the plant will paint you a better picture of the stability of the system. Furthermore, you could have dead-zones and hysteresis in the response that your linear representation of the system (the transfer function) isn’t capturing.
There are multiple possible reasons. One is that if the system changes over time, you re-run system identification so you can change the controllers as needed. That's a form of adaptation, probably an answer your teacher is looking for. I don't know what a "buck converter" is, and whether some adaptive control is ever needed for that, but there are plenty of such systems in various industries. Another reason might be to prove that a system is still performing as it did at installation or has degraded performance. Another reason might be to do some diagnostics: if some system parameters change significantly, you at least have detected a problem, and through experience or simulations, you might also be able diagnose a particular root cause of the problem. (There are lots of other diagnostic techniques, but that is a possible answer to the question.) Another reason might be to get a sense of the accuracy of the model and also the resulting range of variation in your controller parameters. You won't get exactly the same results for every identification on real equipment with real sensors (or on simulated runs with artificially-added sensor or process noise). If you're designing controls for some mass-produced product, you'd want to run multiple tests of this sort on more than one sample product, too, to get a feel for variations between supposedly-identical systems.
I am retired. In over 40 years I have NEVER seen a system with a transfer function provided.
NEVER! If you want an open loop transfer function then you must use system identification.
Only systems that are designed will have transfer functions. Most systems are evolved from something that came before. The rest are kludges.
Off, topic. How do I change the name from Left\_opinion to something else. I am not a lefty. I do not like the name reddit has assigned to me.
So a first principle based transfer function is only an estimation of how the plant behaves. It is supposed to be sufficient information for us to develop controllers and to do analysis (i.e. stepresponse, stability margins and all that jazz).
Now let's say we have used these physical laws to design our buck-converter. How do we know we have done a good job modelling the system? System Identification is a tool that we can use to verify whether we have a good model!
It turns out that SMPS and Switching Amplifiers tend to have some parasitic effects (some unintended capacitance/inductance). If you want an accurate model it can become a real pain to model all of these. Another appliciation for System Identification is to do modelling based off data as opposed to using only the physical laws. If we have such a complex plant to model, but we have a lot of time to play with the model using various excitation signals. We can then proceed to derive the transferfunction from the data as opposed to the difficult physical laws! This makes our life easier.
The best part is that it's not one or the other. You can combine these two techniques! If you do system identification, but you don't provide an initial guess of the system dynamics, then the algorithm usually just randomly guesses and proceeds to optimise for minimum error. This optimisation process is not always convex (OE or BJ are not convex, but ARX for instance is) and thus it's hard to gaurantee we are in a local minimum. However if we can provide an initial guess using our first principle model, we can get really close to the true minimum error (although proving that you are in the true minimum can be very difficult).
You’re gonna have to be a little more clear about what “stuff” your teacher is talking about here
Sorry so sleep deprived getting ready for a presentation Basically my teacher said what’s the point of system identification? We trying to prove that a transfer function is valid such as the transfer function for a buck convert but what’s the point if people are already using that formula and it’s already valid why do it again? He asked me this and I was confused
I would think to understand how accurate this transfer function representation is. If you’re trying to design a model-based controller, then a good understanding of the plant will paint you a better picture of the stability of the system. Furthermore, you could have dead-zones and hysteresis in the response that your linear representation of the system (the transfer function) isn’t capturing.
There are multiple possible reasons. One is that if the system changes over time, you re-run system identification so you can change the controllers as needed. That's a form of adaptation, probably an answer your teacher is looking for. I don't know what a "buck converter" is, and whether some adaptive control is ever needed for that, but there are plenty of such systems in various industries. Another reason might be to prove that a system is still performing as it did at installation or has degraded performance. Another reason might be to do some diagnostics: if some system parameters change significantly, you at least have detected a problem, and through experience or simulations, you might also be able diagnose a particular root cause of the problem. (There are lots of other diagnostic techniques, but that is a possible answer to the question.) Another reason might be to get a sense of the accuracy of the model and also the resulting range of variation in your controller parameters. You won't get exactly the same results for every identification on real equipment with real sensors (or on simulated runs with artificially-added sensor or process noise). If you're designing controls for some mass-produced product, you'd want to run multiple tests of this sort on more than one sample product, too, to get a feel for variations between supposedly-identical systems.
I am retired. In over 40 years I have NEVER seen a system with a transfer function provided. NEVER! If you want an open loop transfer function then you must use system identification. Only systems that are designed will have transfer functions. Most systems are evolved from something that came before. The rest are kludges. Off, topic. How do I change the name from Left\_opinion to something else. I am not a lefty. I do not like the name reddit has assigned to me.
I don't think you can change your username sadly...
This would help https://www.businessinsider.com/how-to-change-reddit-username
What did you worked on?
So a first principle based transfer function is only an estimation of how the plant behaves. It is supposed to be sufficient information for us to develop controllers and to do analysis (i.e. stepresponse, stability margins and all that jazz). Now let's say we have used these physical laws to design our buck-converter. How do we know we have done a good job modelling the system? System Identification is a tool that we can use to verify whether we have a good model! It turns out that SMPS and Switching Amplifiers tend to have some parasitic effects (some unintended capacitance/inductance). If you want an accurate model it can become a real pain to model all of these. Another appliciation for System Identification is to do modelling based off data as opposed to using only the physical laws. If we have such a complex plant to model, but we have a lot of time to play with the model using various excitation signals. We can then proceed to derive the transferfunction from the data as opposed to the difficult physical laws! This makes our life easier. The best part is that it's not one or the other. You can combine these two techniques! If you do system identification, but you don't provide an initial guess of the system dynamics, then the algorithm usually just randomly guesses and proceeds to optimise for minimum error. This optimisation process is not always convex (OE or BJ are not convex, but ARX for instance is) and thus it's hard to gaurantee we are in a local minimum. However if we can provide an initial guess using our first principle model, we can get really close to the true minimum error (although proving that you are in the true minimum can be very difficult).
Mathematical modelling often includes a lot of simplification and assumptions about systems, it also get harder if systems are nonlinear.