|System Identification||Identify models of dynamic systems from measured data|
|Estimate State Space Model Live Editor Task||Estimate state-space model using time or frequency data in the Live Editor|
|Estimate state-space model using time-domain or frequency-domain data|
|Estimate state-space model by reduction of regularized ARX model|
|Estimate state-space model using subspace method with time-domain or frequency-domain data|
|State-space model with identifiable parameters|
|Prediction error estimate for linear and nonlinear model|
|Estimate time delay (dead time) from data|
|Model parameters and associated uncertainty data|
|Modify value of model parameters|
|Obtain attributes such as values and bounds of linear model parameters|
|Set attributes such as values and bounds of linear model parameters|
|Quick configuration of state-space model structure|
|Set or randomize initial parameter values|
|Create parameter for initial states and input level estimation|
|State-space data of identified system|
|Estimate initial states of model|
To estimate a state-space model, you must provide a value of its order, which represents the number of states.
Import data into the System Identification app.
Perform black-box or structured estimation.
The default parameterization of the state-space matrices A, B, C, D, and K is free; that is, any elements in the matrices are adjustable by the estimation routines.
Canonical parameterization represents a state-space system in a reduced parameter form where many elements of A, B and C matrices are fixed to zeros and ones.
Structured parameterization lets you exclude specific parameters from estimation by setting these parameters to specific values.
This example shows how to estimate ARMAX and OE-form models using the state-space estimation approach.
Reduce the order of a Simulink® model by linearizing the model and estimating a lower-order model that retains model dynamics.
State-space models are models that use state variables to describe a system by a set of first-order differential or difference equations, rather than by one or more nth-order differential or difference equations.
You can use time-domain and frequency-domain data that is real or complex and has single or multiple outputs.
System Identification Toolbox™ software supports the following parameterizations that indicate which parameters are estimated and which remain fixed at specific values:
Modal, companion, observable and controllable canonical state-space models.
When you estimate state-space models, you can specify how the algorithm treats initial states.
Choose between noniterative subspace methods, iterative method that uses prediction error minimization algorithm, and noniterative method.
An identified linear model is used to simulate and predict system outputs for given input and noise signals.