모델 유형 및 기타 변환

제어 설계를 위해 모델 유형 변환, 모델 차수 축소

함수

`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

`compreal`	Compute companion state-space realization (R2023b 이후)
`modalreal`	Compute modal state-space realization (R2023b 이후)
`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 linear identified 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`	상태공간 모델의 상태 좌표 변환

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.
상태공간 실현
상태공간 모델은 무한히 많은 실현으로 표현할 수 있습니다. 일반형(표준형이라고도 함)에는 모드형, 동반형, 관측 가능형 및 제어 가능형이 포함됩니다.
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.