Output Error Method for parameter estimation

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M.Many
M.Many el 3 de Feb. de 2021
Comentada: Star Strider el 3 de Feb. de 2021
Dear Matlab community,
I have the following state space model :
The model is defined with : 5 states (X),1 input (U), and 2 outputs (Y)
Hence the matrices are of the following dimensions:
-
This makes a total of 49 parameters.
I have the data that come from testing and are given in the attached .mat file as an iddata object.
Now, i would like to perform system identification to fit the model to the measured output, using the output error method. And finally get the values of the parameters.
I have tried to use the oe() function included in the system identification toolbox, but it doesn't seem to do what i want. Is there anybody who can help me ? Do you know other methods to get the parameters of the fitted model ? Has anyone good experience with system identification ?
Thank you for your time !

Respuesta aceptada

Star Strider
Star Strider el 3 de Feb. de 2021
I would use the ssest function:
D = load('dat.mat');
dat = D.dat;
sys = ssest(dat, 5);
A = sys.A
B = sys.B
C = sys.C
D = sys.D
those being:
A =
-9.8108e-03 -1.7520e-02 2.2364e-02 9.7365e-03 3.0023e-01
-8.5323e-02 -1.5350e-01 -1.7269e+00 7.8601e-02 5.1336e-01
-4.6620e-02 1.4686e+00 -9.1005e+00 1.7038e+00 1.8151e+00
1.7951e-02 -3.4105e-01 -2.9225e+00 -5.5039e+00 7.5581e+01
2.7352e-02 4.1784e-01 4.0000e+00 -4.2111e+01 -6.9225e+01
B =
1.1602e-05
7.3110e-06
-9.9022e-04
5.3061e-03
1.5000e-02
C =
-1.7279e+03 1.3914e+03 -1.3506e+01 -7.0445e-02 -1.4056e-01
2.8143e+04 9.0494e+02 -1.6417e+00 -1.9433e+01 -4.8266e+00
D =
0
0
with:
figure
compare(dat, sys)
producing:
.
  3 comentarios
M.Many
M.Many el 3 de Feb. de 2021
Do you know if there is a way to get the bias parameters with the ssest function ?
Star Strider
Star Strider el 3 de Feb. de 2021
As always, my pleasure!
I’m not certain what ‘bias parameters’ are in this context, so I can’t specifically address that. There are a number of options and name-value pair arguments to get additional information from the model and the estimation procedure, all described in the documentation.

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