Documentation

# findstates

Estimate initial states of model

## Syntax

``x0 = findstates(sys,Data)``
``x0 = findstates(sys,Data,Horizon)``
``x0 = findstates(sys,Data,Horizon,Options)``
``````[x0,Report]= findstates(___)``````

## Description

example

````x0 = findstates(sys,Data)` estimates the initial states, `x0`, of an identified model `sys`, to maximize the fit between the model response and the output signal in the estimation data.```

example

````x0 = findstates(sys,Data,Horizon)` specifies the prediction horizon for computing the response of `sys`.```

example

````x0 = findstates(sys,Data,Horizon,Options)` specifies additional options for computation of `x0`. ```

example

``````[x0,Report]= findstates(___)``` delivers a report on the initial state estimation. `Report` is returned with any of the previous syntaxes.```

## Examples

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Create a nonlinear grey-box model. The model is a linear DC motor with one input (voltage), and two outputs (angular position and angular velocity). The structure of the model is specified by dcmotor_m.m file.

```FileName = 'dcmotor_m'; Order = [2 1 2]; Parameters = [0.24365;0.24964]; nlgr = idnlgrey(FileName,Order,Parameters); nlgr = setinit(nlgr, 'Fixed', false(2,1)); % set initial states free```

Load data for initial state estimation.

```load(fullfile(matlabroot,'toolbox','ident',... 'iddemos','data','dcmotordata')); z = iddata(y,u,0.1);```

Estimate the initial states such that the model's response using the estimated states X0 and measured input u is as close as possible to the measured output y.

`X0 = findstates(nlgr,z,Inf);`

Estimate an `idss` model and simulate it such that the response of the estimated model matches the estimation data's output signal as closely as possible.

`load iddata1 z1;`

Estimate a linear model from the data.

`model = ssest(z1,2);`

Estimate the value of the initial states to best fit the measured output `z1.y`.

`x0est = findstates(model,z1,Inf);`

Simulate the model.

```opt = simOptions('InitialCondition',x0est); sim(model,z1(:,[],:),opt);``` Estimate the initial states of a model selectively by fixing the first state and allowing the second state of the model to be estimated.

Create a nonlinear grey-box model.

```FileName = 'dcmotor_m'; Order = [2 1 2]; Parameters = [0.24365;0.24964]; nlgr = idnlgrey(FileName,Order,Parameters);```

The model is a linear DC motor with one input (voltage), and two outputs (angular position and angular velocity). The structure of the model is specified by `dcmotor_m.m` file.

```load(fullfile(matlabroot,'toolbox','ident',... 'iddemos','data','dcmotordata')); z = iddata(y,u,0.1);```

Hold the first state fixed at zero, and estimate the value of the second.

```x0spec = idpar('x0',[0;0]); x0spec.Free(1) = false; opt = findstatesOptions; opt.InitialState = x0spec; [X0,Report] = findstates(nlgr,z,Inf,opt)```
```X0 = 2×1 0 0.0061 ```
```Report = Status: 'Estimated by simulation error minimization' Method: 'lsqnonlin' Covariance: [2x2 double] DataUsed: [1x1 struct] Termination: [1x1 struct] ```

Create a nonlinear grey-box model.

```FileName = 'dcmotor_m'; Order = [2 1 2]; Parameters = [0.24365;0.24964]; nlgr = idnlgrey(FileName,Order,Parameters);```

The model is a linear DC motor with one input (voltage), and two outputs (angular position and angular velocity). The structure of the model is specified by `dcmotor_m.m` file.

```load(fullfile(matlabroot,'toolbox','ident',... 'iddemos','data','dcmotordata')); z = iddata(y,u,0.1);```

Specify an initial guess for the initial states.

`x0spec = idpar('x0',[10;10]);`

`x0spec.Free` is true by default

Estimate the initial states

```opt = findstatesOptions; opt.InitialState = x0spec; x0 = findstates(nlgr,z,Inf,opt)```
```x0 = 2×1 0.0362 -0.1322 ```

Create a nonlinear grey-box model.

```FileName = 'dcmotor_m'; Order = [2 1 2]; Parameters = [0.24365;0.24964]; nlgr = idnlgrey(FileName,Order,Parameters); set(nlgr, 'InputName','Voltage','OutputName', ... {'Angular position','Angular velocity'});```

The model is a linear DC motor with one input (voltage), and two outputs (angular position and angular velocity). The structure of the model is specified by `dcmotor_m.m` file.

```load(fullfile(matlabroot,'toolbox','ident',... 'iddemos','data','dcmotordata')); z = iddata(y,u,0.1,'Name','DC-motor',... 'InputName','Voltage','OutputName',... {'Angular position','Angular velocity'});```

Create a three-experiment data set.

`z3 = merge(z,z,z);`

Choose experiment for estimating the initial states:

• Estimate initial state 1 for experiments 1 and 3

• Estimate initial state 2 for experiment 1

The fixed initial states have zero values.

```x0spec = idpar('x0',zeros(2,3)); x0spec.Free(1,2) = false; x0spec.Free(2,[2 3]) = false; opt = findstatesOptions; opt.InitialState = x0spec;```

Estimate the initial states

`[X0,EstInfo] = findstates(nlgr,z3,Inf,opt);`

## Input Arguments

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Identified model whose initial states are estimated, represented as a linear state-space (`idss` or `idgrey`) or nonlinear model (`idnlarx`, `idnlhw`, or `idnlgrey`).

Estimation data, specified as an `iddata` object with input/output dimensions that match `sys`.

If `sys` is a linear model, `Data` can be a frequency-domain `iddata` object. For easier interpretation of initial conditions, make the frequency vector of `Data` be symmetric about the origin. For converting time-domain data into frequency-domain data, use `fft` with `'compl'` input argument, and ensure that there is sufficient zero padding. Scale your data appropriately when you compare `x0` between the time-domain and frequency-domain. Since for an N-point fft, the input/output signals are scaled by `1/sqrt(N)`, the estimated `x0` vector is also scaled by this factor.

Prediction horizon for computing the response of `sys`, specified as a positive integer between `1` and `Inf`. The most common values used are:

• `Horizon = 1` — Minimizes the 1-step prediction error. The 1–step ahead prediction response of `sys` is compared to the output signals in `Data` to determine `x0`. See `predict` for more information.

• `Horizon = Inf` — Minimizes the simulation error. The difference between measured output, `Data.y`, and simulated response of `sys` to the measured input data, `Data.u` is minimized. See `sim` for more information.

Specify `Horizon` as any positive integer between 1 and `Inf`, with the following restrictions:

Scenario`Horizon`

Continuous-time model with time-domain data

`1` or `Inf`

Continuous-time frequency-domain data (`data.Ts` = 0)

`Inf`

Output Error models (trivial noise component):

Irrelevant

Any value of `Horizon` returns the same answer for `x0`

Nonlinear ARX (`idnlarx`)`1` or `Inf`

Estimation options for `findstates`, specified as an option set created using `findstatesOptions`

## Output Arguments

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Estimated initial states of model `sys`, returned as a vector or matrix. For multi-experiment data, `x0` is a matrix with one column for each experiment.

Initial state estimation information, returned as a structure. `Report` contains information about the data used, state covariance, and results of any numerical optimization performed to search for the initial states. `Report` has the following fields:

Report FieldDescription
`Status`

Summary of how the initial state were estimated.

`Method`

Search method used.

`Covariance`

Covariance of state estimates, returned as a Ns-by-Ns matrix, where Ns is the number of states.

`DataUsed`

Attributes of the data used for estimation, returned as a structure with the following fields:

FieldDescription
`Name`

Name of the data set.

`Type`

Data type.

`Length`

Number of data samples.

`Ts`

Sample time.

`InterSample`

Input intersample behavior, returned as one of the following values:

• `'zoh'` — Zero-order hold maintains a piecewise-constant input signal between samples.

• `'foh'` — First-order hold maintains a piecewise-linear input signal between samples.

• `'bl'` — Band-limited behavior specifies that the continuous-time input signal has zero power above the Nyquist frequency.

`InputOffset`

Offset removed from time-domain input data during estimation. For nonlinear models, it is `[]`.

`OutputOffset`

Offset removed from time-domain output data during estimation. For nonlinear models, it is `[]`.

`Termination`

Termination conditions for the iterative search used for initial state estimation of nonlinear models. Structure with the following fields:

FieldDescription
`WhyStop`

Reason for terminating the numerical search.

`Iterations`

Number of search iterations performed by the estimation algorithm.

`FirstOrderOptimality`

$\infty$-norm of the gradient search vector when the search algorithm terminates.

`FcnCount`

Number of times the objective function was called.

`UpdateNorm`

Norm of the gradient search vector in the last iteration. Omitted when the search method is `'lsqnonlin'` or `'fmincon'`.

`LastImprovement`

Criterion improvement in the last iteration, expressed as a percentage. Omitted when the search method is `'lsqnonlin'` or `'fmincon'`.

`Algorithm`

Algorithm used by `'lsqnonlin'` or `'fmincon'` search method. Omitted when other search methods are used.

`Termination` is empty for linear models.

## Extended Capabilities 