User`s guide

3 Linear Model Identification
Tip Continuous state-space models are available for canonical and
structured parameterizations and grey-box models. In this case, no
disturbance model can be estimated.
Designating Data for Estimating Discrete-Time Models
You can estimate arbitrar y-ord er, linear state-space models for both time- or
frequency-domain data.
You must specify your data to have the sampling interval equal to the
experimental data sampling interval.
You can set the sampling interval when you import data into the GUI or set
the
Ts property of the data object at the command line.
Supported State-Space P arameterizations
The System Identification Toolbox product supports the following
parameterizations that indicate w hich parameters are estimated and which
remain fixed at specificvalues:
• Free parameteriz ation results in the estimation of all system matrix
elements A, B, C, D,andK.
• Canonical forms of A, B, C, D,andK matrices.
Canonical parameterization represents a state-space system in its minimal
form, using the minimum number o f free parameters to capture the
dynamics. Thus, free parameters appear in only a few of the rows and
columns in system matrices A, B , C,andD, and the remaining matrix
elements are fixedtozerosandones.
• Structured parameterization lets you specify the values of specific
parameters and exclude these parameters from estimation.
• Completely arbitrary mapping of parameters to s tate-space matrices. For
more information, see “Estimating Linear Grey-Box Models” on page 5-6.
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