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Suppose I have a sytem with the following transfer function :

$$H(s) = \frac{N_H(s)}{D_H(s)}$$

I would like a general method which is not dependant on the order of the system to analyze what would be the step response.

I know what is my transfer function and the order of the system is high (superior to 5). In this transfer function I would like to modify certain parameters to see how evolve the response time. Nevertheless I do not want to make my eyes work and say by looking the step response "Well that is what I wanted to have " . I want something that could optimize the different parameters via a programm. I was thinking of using the quality factor but I do not how to evaluate it on a hight order transfer function. Do you have an ider or other idea that could help me ? (I would prefer to use python rather than Matlab)

Thank you very much and have a nice day :)

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    $\begingroup$ "system identification" is the term you'd want to research. Large topic! Knowing the step response is "identical" to knowing the impulse response is "identical" to knowing the Laplace domain response, i.e. a full description of your system. $\endgroup$
    – Marcus Müller
    Commented Jan 31, 2021 at 12:37
  • $\begingroup$ Ok I will try to find some information via "System Identification" ;) Thank you ;) $\endgroup$
    – Jess
    Commented Jan 31, 2021 at 13:47
  • $\begingroup$ Have you any documentation ? or website where I can find some information ? I suppose I can get some informations if I look at the poles in the s domain ? $\endgroup$
    – Jess
    Commented Jan 31, 2021 at 14:18
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    $\begingroup$ en.wikipedia.org/wiki/System_identification look through the books under "further reading" at the end of the article (wikipedia is a very logical first step in looking for resources, not always the best, but usually gives you a start). $\endgroup$
    – Marcus Müller
    Commented Jan 31, 2021 at 14:21
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    $\begingroup$ Yes, going through that list and reading book descriptions to find the book that fits your background, not mine! $\endgroup$
    – Marcus Müller
    Commented Jan 31, 2021 at 17:23

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