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I already get a point series of the bode plot,(that is, system gain in frequency domain),{{freq1, gain1},{freq2,gain2}....} named as $data$

So I first assume that the transfer function is in the form of $$model=\frac{a1s^2+b1s+c1}{a2s^2+b2s+c2}$$ (by analyzing the bode plot, the tf should be in this form)

and then I use $FindFit$ function to estimate parameters $vars=\{a1,b1,c1,a2, b2,c2\}$

$$FindFit[data,Abs@model,vars,s]$$ however what I get is totally wrong, the fitting result gives a very high peak around 0 and never match the shape of original bode plot.

so, can you guys give some advice on how to estimate transfer function with mathematica

I know matlab has tfest. however, in my case, for some reason, it is better to do that is mathematica

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Here is an example showing my process, with some simulation data, not the real experimental one.

fitmodel=Abs[n1/(a*(s*I)^4 + b*(s*I)^3 + c*(s*I)^2 + d*(s*I) + e)];
par={n1, a, b, c, d, e};
givenpar = Thread[par -> {500000, 1, 30, 26300, 270000, 25000000}];
givengain = Table[fitmodel /. givenpar, {s, 1, 300, 1}];
fitpar = FindFit[givengain, fitmodel, par, s];
fitgain = Table[fitmodel /. fitpar, {s, 1, 300, 1}];

result

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  • $\begingroup$ Please show your exact code. Among other things, Abs@model looks quite suspicious. $\endgroup$ – bill s Oct 31 '16 at 3:04
  • $\begingroup$ I do this very regularly. What is your data? Is it complex or just modulus values of the transfer function along the imaginary axis? Note that it is usual to set a2 = 1. As a minimum working example can you write a short code to generate some data? $\endgroup$ – Hugh Oct 31 '16 at 10:01
  • $\begingroup$ @Hugh Hi I have updated the question, with a small example showing my proceess $\endgroup$ – lorty Nov 1 '16 at 15:40
  • $\begingroup$ @bills code updated, but not the whole, just a smal example to show what i did and what i got $\endgroup$ – lorty Nov 1 '16 at 15:43
  • $\begingroup$ Give the algorithm a sensible starting place and it converges fine. I used {{n1, 480000}, {a, 1}, {b, 25}, {c, 25000}, {d, 250000}, {e, 26000000}} $\endgroup$ – bill s Nov 1 '16 at 15:52
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The issue you have is with initial estimates (as noted by bill s). First I look at your data.

fitmodel = Abs[n1/(a*(s*I)^4 + b*(s*I)^3 + c*(s*I)^2 + d*(s*I) + e)];
par = {n1, a, b, c, d, e};
givenpar = Thread[par -> {500000, 1, 30, 26300, 270000, 25000000}];
givengain = Table[fitmodel /. givenpar, {s, 1, 300, 1}] // N; data = 
 ListPlot[givengain, PlotRange -> All]

Mathematica graphics

This looks like two resonant modes with one resonance at about 25 and the other at about 150. To help getting good starting estimates I suggest you formulate your model in the form

model = a/Abs[( s^2 + b1 s + c1) ( s^2 + b2 s + c2)] /.s -> I ω

(* a/Abs[(c1 + I b1 ω - ω^2) (c2 + I b2 ω - ω^2)]  *)

Note that I don't have a coefficient on the highest power of s in the denominator. The OP does but this cancels with a factor of the term in the numerator and is not necessary. It is now easier to suggest starting values; c1 and c2 are the resonance frequencies squared we have already estimated. The values for b1 and b2 can be estimated from the sharpness of the peaks -I will take a guess. Now I fit using NonlinearModelFit which I prefer to FindFit because it gives good properties of the fit.

fit = NonlinearModelFit[givengain, 
   model, {a, {b1, 0.1}, {c1, 25^2}, {b2, 0.1}, {c2,150^2}}, ω];
fit["ParameterConfidenceIntervalTable"]

Mathematica graphics

You can see that the parameters have been found with only numerical error. I am concerned that b1 is negative but this is the consequence of fitting the absolute values. If you have complex values you should fit those. Otherwise you may need to change the sign on the b values.

We can compare the model and the fit

Show[
 data,
 Plot[res[ω], {ω, 1, 300}, PlotRange -> All]
 ]

Mathematica graphics

The model goes exactly through the data.

It is straightforward to convert my coefficients to yours but this is not the issue here. The problem is how to find good initial estimates. I often use some peak identification algorithm to find the frequency of the resonances and use these as the estimates.

Hope that helps.

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