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Estimate state-space model using time-domain or frequency-domain data

estimates a continuous-time state-space model `sys`

= ssest(`data`

,`nx`

)`sys`

of order
`nx`

, using data `data`

that can be in the time
domain or the frequency domain. `sys`

is a model of the following
form:

$$\begin{array}{l}\dot{x}(t)=Ax(t)+Bu(t)+Ke(t)\\ y(t)=Cx(t)+Du(t)+e(t)\end{array}$$

*A*, *B*, *C*,
*D*, and *K* are state-space matrices.
*u*(*t*) is the input,
*y*(*t*) is the output,
*e*(*t*) is the disturbance, and
*x*(*t*) is the vector of `nx`

states.

All entries of *A*, *B*, *C*, and
*K* are free estimable parameters by default. *D* is
fixed to zero by default, meaning that there is no feedthrough, except for static systems
(`nx = 0`

).

incorporates additional options specified by one or more name-value pair arguments. For
example, estimate a discrete-time model by specifying the sample time
`sys`

= ssest(`data`

,`nx`

,`Name,Value`

)`'Ts'`

name-value pair argument. Use the `'Form'`

,
`'Feedthrough'`

, and `'DisturbanceModel'`

name-value pair arguments to modify the default behavior of the *A*,
*B*, *C*, *D*, and
*K* matrices.

`ssest`

initializes the parameter estimates using either a noniterative
subspace approach or an iterative rational function estimation approach. It then refines the
parameter values using the prediction error minimization approach. For more information, see
`pem`

and `ssestOptions`

.

[1] Ljung, L. *System Identification: Theory for the
User*, Second Edition. Upper Saddle River, NJ: Prentice Hall PTR,
1999.

`canon`

|`iddata`

|`idfrd`

|`idgrey`

|`idss`

|`n4sid`

|`pem`

|`polyest`

|`procest`

|`ssestOptions`

|`ssregest`

|`tfest`

- Estimate State-Space Models at the Command Line
- Estimate State-Space Models with Free-Parameterization
- Estimate State-Space Models with Canonical Parameterization
- Estimate State-Space Models with Structured Parameterization
- Use State-Space Estimation to Reduce Model Order
- What Are State-Space Models?
- Supported State-Space Parameterizations
- State-Space Model Estimation Methods
- Regularized Estimates of Model Parameters
- Estimating Models Using Frequency-Domain Data