|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. When you do not know the order, you can search and select an order using the following procedures.
Import data into the System Identification app. See データの表現. For supported data formats, see Data Supported by State-Space Models.
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. Because the parameterization of A, B, and C is free, a basis for the state-space realization is automatically selected to give well-conditioned calculations.
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. The free parameters appear in only a few of the rows and columns in state-space matrices A, B, C, D, and K. The free parameters are identifiable — they can be estimated to unique values. The remaining matrix elements are fixed to zeros and ones.
Structured parameterization lets you exclude specific parameters from estimation by setting these parameters to specific values. This approach is useful when you can derive state-space matrices from physical principles and provide initial parameter values based on physical insight. You can use this approach to discover what happens if you fix specific parameter values or if you free certain parameters.
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. State variables x(t) can be reconstructed from the measured input-output data, but are not themselves measured during an experiment.
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. This information supports the estimation procedures Estimate State-Space Models in System Identification App and Estimate State-Space Models at the Command Line.
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. The input signals are measured while the noise signals are only known via their statistical mean and variance. The general form of the state-space model, often associated with Kalman filtering, is an example of such a model, and is defined as: