Main Content

Model Type and Other Transformations

Convert model type for control design, reduce model order


idfrdFrequency response data or model
idpolyPolynomial model with identifiable parameters
idtfTransfer function model with identifiable parameters
idssState-space model with identifiable parameters
canonCanonical state-space realization
balredModel order reduction
noisecnvTransform identified linear model with noise channels to model with measured channels only
translatecovTranslate parameter covariance across model transformation operations
mergeMerge estimated models
appendGroup models by appending their inputs and outputs
noise2measNoise component of model
absorbDelayReplace time delays by poles at z = 0 or phase shift
chgTimeUnitChange time units of dynamic system
chgFreqUnitChange frequency units of frequency-response data model
fdelDelete specified data from frequency response data (FRD) models
stackBuild model array by stacking models or model arrays along array dimensions
ss2ssState 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 of linear identified models 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.

Canonical State-Space Realizations

Modal, companion, observable and controllable canonical state-space models.

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

Convert noise channels to measured channels and include the variance of the innovations.