Model Type and Other Transformations

Convert model type for control design, reduce model order

Functions

`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`	Canonical state-space 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 transformation 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

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

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

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

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

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

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

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

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

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