Problems using 'tfest' for generating transfer function
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Hello guys...
I am trying to find a transfer function for my signal, that matches with the moving average block of my signal. I used tfest to recreate the signal using the generated coefficients. However, the accuracy is very low at 2%. I am wondering how to increase it, or am I doing something wrong.
In another case, to verify whether the function works, I tried with tf = 1/(s+1), generated the signals, used 'tfest' to get the transfer function, I got 0.1404/(s+0.1998) , which is way-way off.
let me know what you think of this.
Have a nice day..
data_0= iddata(T_avg_mean,T_pedal,0.01);
>> tf_estimated = tfest(data_0,2)
tf_estimated =
From input "u1" to output "y1":
-0.0188 s + 0.005945
--------------------------
s^2 + 7.891e-06 s + 0.4232
Continuous-time identified transfer function.
Parameterization:
Number of poles: 2 Number of zeros: 1
Number of free coefficients: 4
Use "tfdata", "getpvec", "getcov" for parameters and their uncertainties.
Status:
Estimated using TFEST on time domain data "data_0".
Fit to estimation data: 2.051%
FPE: 128.3, MSE: 128.3

10 Comments
Mathieu NOE
on 7 Nov 2022
hello
can you share the data you used for tfest ?
RITAM BASU
on 7 Nov 2022
Mathieu NOE
on 7 Nov 2022
hello again
the simulink file I could not open it causeI run R2020b
maybe I simply need both input and output data but your T_in timeseries has only one data
RITAM BASU
on 7 Nov 2022
Mathieu NOE
on 16 Nov 2022
hello
problem solved ?
RITAM BASU
on 16 Nov 2022
RITAM BASU
on 16 Nov 2022
Mathieu NOE
on 17 Nov 2022
hello again
I put the comment in the answer section as you kindly suggested
now I don't understand your problems with my scripts ...remember that the identified transfer functions are discrete models so the B and A are coefficients you cannot compare to a continuous time model like (1/(s+1))
you have to convert the identified discrete models to continuous time model with d2c function (I didn't make it in my script but you can easily add that code)
RITAM BASU
on 28 Nov 2022
Mathieu NOE
on 28 Nov 2022
My pleasure !
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