Main Content

You can estimate state-space models in two ways at the command line, depending upon your prior knowledge of the nature of the system and your requirements.

In this approach, you specify the model order, and, optionally, additional model structure
attributes that configure the overall structure of the state-space matrices. You call `ssest`

, `ssregest`

or `n4sid`

with data and model order as primary input arguments, and use name-value
pairs to specify any additional attributes, such as model sample time, presence of feedthrough,
absence of noise component, etc. You do not work directly with the coefficients of the
*A*, *B*, *C*, *D*,
*K*, and *X0* matrices.

In this approach, you create and configure an `idss`

model that contains the initial values for all the system matrices. You use
the `Structure`

property of the `idss`

model to specify all
the parameter constraints. For example, you can designate certain coefficients of system
matrices as fixed and impose minimum/maximum bounds on the values of the others. For quick
configuration of the parameterization and whether to estimate feedthrough and disturbance
dynamics, use `ssform`

.

After configuring the `idss`

model with desired constraints, you
specify this model as an input argument to the `ssest`

command. You cannot use `n4sid`

or `ssregest`

for structured estimation.

**Note**

The structured estimation approach is also referred to as grey-box modeling. However, in this toolbox, the “grey box modeling” terminology is used only when referring to

`idgrey`

and`idnlgrey`

models.Using the structured estimation approach, you cannot specify relationships among state-space coefficients. Each coefficient is essentially considered to be independent of others. For imposing dependencies, or to use more complex forms of parameterization, use the

`idgrey`

model and`greyest`

estimator.

You can estimate continuous-time and discrete-time state-space models using the iterative
estimation command `ssest`

that minimizes the prediction errors to obtain
maximum-likelihood values.

Use the following general syntax to both configure and estimate state-space models:

m = ssest(data,n,opt,Name,Value)

where `data`

is the estimation data, `n`

is the model
order, and `opt`

contains options for configuring the estimation of the
state-space models. These options include the handling of the initial conditions, input and
output offsets, estimation focus and search algorithm options. opt can be followed by name-value
pair input arguments that specify optional model structure attributes such as the presence of
feedthrough, the canonical form of the model, and input delay.

As an alternative to `ssest`

, you can use the noniterative subspace
estimators `n4sid`

or `ssregest`

:

m = n4sid(data,n,opt,Name,Value) m = ssregest(data,n,opt,Name,Value)

Unless you specify the sample time as a name-value pair input argument,
`n4sid`

and `ssregest`

estimate a discrete-time model,
while `ssest`

estimates a continuous-time model.

**Note**

`ssest`

uses `n4sid`

to initialize the state-space
matrices, and takes longer than `n4sid`

to estimate a model but typically
provides a better fit to data.

For information about validating your model, see Validating Models After Estimation

By default, all entries of the *A*, *B*, and
*C* state-space matrices are treated as free parameters. Using the
`Form`

name-value pair input argument of `ssest`

, you can choose various canonical forms, such as the companion and modal
forms, that use fewer parameters.

For more information about estimating a specific state-space parameterization, see:

For estimation of state-space models, you have the option of switching the model sample
time between zero and that of the estimation data. You can do this using the
`Ts`

name-value pair input argument.

By default,

`ssest`

estimates a continuous-time model. If you are using data set with nonzero sample time,`data`

, which includes all time domain data, you can also estimate a discrete-time model by using:`model = ssest(data,nx,'Ts',data.Ts);`

If you are using continuous-time frequency-domain data, you cannot estimate a discrete-time model.

By default,

`n4sid`

and`ssregest`

estimate a model whose sample time matches that of the data. Thus, for time-domain data,`n4sid`

and`ssregest`

deliver a discrete-time model. You can estimate a continuous-time model by using:`model = n4sid(data,nx,'Ts',0);`

or

`model = ssregest(data,nx,'Ts',0);`

For state-space models with any parameterization, you can specify whether to estimate the
*D*, *K* and *X0* matrices, which
represent the input-to-output feedthrough, noise model and the initial states,
respectively.

For state-space models with structured parameterization, you can also specify to estimate
the *D* matrix. However, for free and canonical forms, the structure of the
*D* matrix is set based on your choice for the
`'Feedthrough'`

name-value pair input argument.

By default, the *D* matrix is not estimated and its value is fixed to
zero, except for static models.

**Black box estimation:**Use the`Feedthrough`

name-value pair input argument to denote the presence or absence of feedthrough from individual inputs. For example, in case of a two input model such that there is feedthrough from only the second input, use:`model = n4sid(data,n,'Feedthrough',[false true]);`

**Structured estimation:**Configure the values of the`init_sys.Structure.D`

, where`init_sys`

is an`idss`

model that represents the desired model structure. To force no feedthrough for the*i*-th input, set:init_sys.Structure.D.Value(:,i) = 0; init_sys.Structure.D.Free = true; init_sys.Structure.D.Free(:,i) = false;

The first line specifies the value of the

*i*-th column of D as zero. The next line specifies all the elements of*D*as free, estimable parameters. The last line specifies that the*i*-th column of the*D*matrix is fixed for estimation.Alternatively, use

`ssform`

with`'Feedthrough'`

name-value pair.

*K* represents the noise matrix of the model, such that the noise
component of the model is:.

$$\begin{array}{l}\dot{x}=Ax+Ke\\ {y}_{n}=Cx+e\end{array}$$

For frequency-domain data, no noise model is estimated and *K* is set
to 0. For time-domain data, *K* is estimated by default in the black box
estimation setup. *y ^{n}* is the contribution of the
disturbances to the model output.

**Black box estimation:**Use the`DisturbanceModel`

name-value pair input argument to indicate if the disturbance component is fixed to zero (specify`Value = 'none'`

) or estimated as a free parameter (specify`Value = 'estimate'`

). For example, use :model = n4sid(data,n,'DisturbanceModel','none');

**Structured estimation:**Configure the value of the`init_sys.Structure.K`

parameter, where`init_sys`

is an`idss`

model that represents the desired model structure. You can fix some*K*matrix coefficients to known values and prescribe minimum/maximum bounds for free coefficients. For example, to estimate only the first column of the*K*matrix for a two output model:`kpar = init_sys.Structure.K; kpar.Free(:,1) = true; kpar.Free(:,2) = false; kpar.Value(:,2) = 0; % second column value is fixed to zero init_sys.Structure.K = kpar;`

Alternatively, use

`ssform`

.

When not sure how to easily fix or free all coefficients of *K*,
initially you can omit estimating the noise parameters in *K* to focus on
achieving a reasonable model for the system dynamics. After estimating the dynamic model, you
can use `ssest`

to refine the model while configuring the
*K* parameters to be free. For example:

init_sys = ssest(data, n,'DisturbanceModel','none'); init_sys.Structure.K.Free = true; sys = ssest(data,init_sys);

where `init_sys`

is the dynamic model without noise.

To set *K* to zero in an existing model, you can set its
`Value`

to `0`

and `Free`

flag to
`false`

:

m.Structure.K.Value = 0; m.Structure.K.Free = false;

The initial state vector *X0* is obtained as the by-product of model
estimation. The `n4sid`

, `ssest`

and
`ssregest`

commands return the value of *X0* as their
second output arguments. You can choose how to handle initial conditions during model
estimation by using the `InitialState`

estimation option. Use `n4sidOptions`

(for `n4sid`

), `ssestOptions`

(for `ssest`

) or `ssregestOptions`

(for `ssregest`

) to create the estimation
option set. For example, in order to hold the initial states to zero during estimation using
`n4sid`

:

```
opt = n4sidOptions;
opt.InitialState = 'zero';
[m,X0] = n4sid(data,n,opt);
```

The returned `X0`

variable is a zero vector of length
`n`

.

When you estimate models using multiexperiment data, the `X0`

matrix
contains as many columns as data experiments.

For a complete list of values for the `InitialStates`

option, see Specifying Initial States for Iterative Estimation Algorithms.