This is machine translation

Translated by Microsoft
Mouse over text to see original. Click the button below to return to the English verison of the page.

Model Type and Other Transformations

Convert model type for control design, reduce model order


idfrd Frequency-response data or model
idpoly Polynomial model with identifiable parameters
idtf Transfer function model with identifiable parameters
idss State-space model with identifiable parameters
canon State-space canonical realization
balred Model order reduction
noisecnv Transform identified linear model with noise channels to model with measured channels only
translatecov Translate parameter covariance across model operations
merge Merge estimated models
append Group models by appending their inputs and outputs
noise2meas Noise component of model
absorbDelay Replace time delays by poles at z = 0 or phase shift
chgTimeUnit Change time units of dynamic system
chgFreqUnit Change frequency units of frequency-response data model
fdel Delete specified data from frequency response data (FRD) models
stack Build model array by stacking models or model arrays along array dimensions
ss2ss State coordinate transformation for state-space model

Examples and How To

Transforming Between Linear Model Representations

Converting between state-space, polynomial, and frequency-response representations.

Reducing Model Order Using Pole-Zero Plots

You can use pole-zero plots to evaluate whether it might be useful to reduce model order.

Create and Plot Identified Models Using Control System Toolbox Software

Identify models and use the Linear System Analyzer to plot the models.


Using Identified Models for Control Design Applications

Using System Identification Toolbox™ models with Control System Toolbox™ software.

Subreferencing Models

Creating models with subsets of inputs and outputs from multivariable models at the command line.

Concatenating Models

Horizontal and vertical concatenation of model objects at the command line.

Merging Models

How to merge models to obtain a single model with parameters that are statistically weighed means of the parameters of the individual models.

Treating Noise Channels as Measured Inputs

Study noise contributions in more detail.

Was this topic helpful?