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Simulate Identified Model in金宝app

After estimating a model at the command line or in theSystem Identificationapp, you can import the model from the MATLAB®workspace into Simulink®using model blocks. You can then simulate the model output for the initial conditions and the model inputs that you specify.

Add model simulation blocks to your Simulink model from the System Identification Toolbox™ block library when you want to:

  • Represent the dynamics of a physical component in a Simulink model using a data-based model

  • Replace a complex Simulink subsystem with a simpler data-based model

Summary of Simulation Blocks

The following model blocks are available in the System Identification Toolbox library in Simulink.

Block Description
Idmodel Simulate a linear identified model in Simulink software. The model can be a process (idproc), linear polynomial (idpoly), state-space (idss), grey-box (idgrey), or transfer function (idtf) model.
Nonlinear ARX Model Simulateidnlarxmodel in Simulink.
Hammerstein-Wiener Model Simulateidnlhwmodel in Simulink.
Nonlinear Grey-Box Model Simulate nonlinear ODE (idnlgreymodel object) in Simulink.

In any of these model blocks, you specify the model-variable name of the identified model you want to import. You also specify initial conditions for simulation (seeSpecifying Initial Conditions for Simulation。) You can specify time-domain input data by:

  • Using aFrom Workspaceblock, if one sample of input data is a scalar or vector

  • Using anIddata Sourceblock, if the input data is in aniddataobject

For detailed information about how to configure the blocks, see the corresponding block reference pages.

Specifying Initial Conditions for Simulation

When your model is not at rest at the start of the simulation, you must specify the initial conditions. The model may not start at rest, for example, when you are:

  • Comparing your model response with measured output data or a previous simulation

  • Continuing from a previous simulation

  • Initiating your simulation during the steady-state phase

  • Initiating your simulation from a predetermined operating condition

If you do not specify initial conditions, you introduce an error source that impacts the apparent performance of your model. This error may be transient; in a model that is lightly damped or includes an integral component, the error may not completely die out.

Specifying initial conditions requires two steps:

  1. Determine what the initial conditions should be (seeEstimate Initial Conditions for Simulating Identified Models)

  2. Specify these values as parameters in the model block.

If you have a linear model that is notidssoridgrey, you also have to convert the model into state-space form before the first step.

If you have already performed a simulation usingcompare,sim, or theSystem Identificationapp, and you want to check out your Simulink implementation by precisely reproducing your earlier results, seeReproduce Command Line or System Identification App Simulation Results in Simulink。

Specifying Initial States of Linear Models

为整数线性指定初始状态odels (idss,idgrey), use theInitial states (state space only: idss, idgrey)parameter in theIdmodelblock. Initial states must be a vector of length equal to the order of the model.

Other types of linear model, such as transfer-function form models (idtf), do not use an explicit state representation. The model blocks therefore have no input for initial state specification; the software assumes initial conditions of zero. To specify initial conditions for one of these models, first convert your modelminto state-space formmssat the command line.

mss = idss(m);
Then usemssin theIdentified modelparameter of theIdmodelblock. You can now specify your initial states as described in the previous paragraph for state-space models.

Simulate Identified Linear Model in Simulink with Initial Conditions

This example uses:

Open Script

This example shows how to set the initial states for simulating a linear model such that the simulation provides a best fit to measured input-output data.

You first estimate a modelMusing a multiple-experiment data setZ, which contains data from three experiments -z1,z2, andz3。

Load the multi-experiment data.

load(fullfile(matlabroot,'toolbox','ident','iddemos',。..'data',“太瓦obodiesdata'))

Create aniddata对象存储multi-experiment数据。集'Tstart'to 0 so that the data start time matches the Simulink start time 0f 0 s.

z1=iddata(y1,u1,0.005,'Tstart',0); z2=iddata(y2,u2,0.005,'Tstart',0); z3=iddata(y3,u3,0.005,'Tstart',0); Z = merge(z1,z2,z3);

Estimate a 5th order state-space model.

[M,x0] = n4sid(Z,5);

To simulate the model using inputu2, usex0(:,2)as the initial states.x0(:,2)is computed to maximize the fit between the measured outputy2and the simulated response ofM。

Extract the initial states that maximize the fit to the corresponding outputy2, and simulate the model in Simulink using the second experiment,z2。

X0est = x0(:,2);

Open a preconfigured Simulink model.

mdl ='ex_idmodel_block'; open_system(mdl)

The model uses the Iddata Source, Idmodel, and Scope blocks. The following block parameters have been preconfigured to specify the simulation data, estimated model, and initial conditions.

Block parameters of Iddata Source block:

  • IDDATA Object-z2

Block parameters of Idmodel block:

  • Identified Model-M

  • Initial states-X0est

Simulate the model for two seconds, and compare the simulated outputysimwith the measured outputymeasuredusing the Scope block.

simOut = sim(mdl); open_system([mdl/范围的])

Compare this result with the result you would have gotten without setting initial conditions. You can nullifyX0in theInitial Statesfield of the Simulink model block by replacing theX0estvariable with0。你可以also nullify X0est itself in command line, as shown here.

X0est = 0*X0est;

Run the simulation, and view the new results.

simOut = sim(mdl); open_system([mdl/范围的])

In this plot, the simulated output has a significant transient at the start, but this transient settles out in time.

Specifying Initial States of Hammerstein-Wiener Models

The states of a Hammerstein-Wiener model correspond to the states of the embedded linear (idpolyoridss) model. For more information about the states of a Hammerstein-Wiener model, see theidnlhwreference page.

The default initial state for simulating a Hammerstein-Wiener model is 0. For more information about specifying initial conditions for simulation, see theIDNLHW Modelreference page.

Simulate Hammerstein-Wiener Model in Simulink

This example uses:

Open Script

Compare the simulated output of a Hammerstein-Wiener Model block to the measured output of a system. You improve the agreement between the measured and simulated responses by estimating initial state values.

Load the sample data.

loadtwotankdata

Create aniddataobject from the sample data. Set'Tstart'to 0 so that the data start time matches the Simulink start time 0f 0 s.

z1 = iddata(y,u,0.2,'Tstart',0,'Name','Two tank system');

Estimate a Hammerstein-Wiener model using the data.

mw1 = nlhw(z1,[1 5 3],idPiecewiseLinear,idPiecewiseLinear);

你可以now simulate the output of the estimated model in Simulink using the input data inz1。To do so, open a preconfigured Simulink model.

model ='ex_idnlhw_block'; open_system(model);

The model uses the Iddata Source, Hammerstein-Wiener Model, and Scope blocks. The following block parameters have been preconfigured to specify the estimation data, estimated model, and initial conditions:

Block parameters of Iddata Source block:

  • IDDATA Object-z1

Block parameters of Hammerstein-Wiener Model block:

  • Model-mw1

  • Initial conditions-Zero(default)

Run the simulation.

View the difference between measured output and model output by using the Scope block.

simOut = sim(model); open_system([model/范围的])

To improve the agreement between the measured and simulated responses, estimate an initial state vector for the model from the estimation dataz1, usingfindstates。Specify the maximum number of iterations for estimation as 100. Specify the prediction horizon asInf, so that the algorithm computes the initial states that minimize the simulation error.

opt = findstatesOptions; opt.SearchOptions.MaxIterations = 100; x0 = findstates(mw1,z1,Inf,opt);

集theInitial conditionsblock parameter value of the Hammerstein-Wiener Model block toState Values。默认的初始状态x0。

set_param([model'/Hammerstein-Wiener Model'],'IC','State values');

Run the simulation again, and view the difference between measured output and model output in the Scope block. The difference between the measured and simulated responses is now reduced.

simOut = sim(model);

Specifying Initial States of Nonlinear ARX Models

The states of a nonlinear ARX model correspond to the dynamic elements of the nonlinear ARX model structure, which are the model regressors.Regressorscan be the delayed input/output variables (standard regressors) or user-defined transformations of delayed input/output variables (custom regressors). For more information about the states of a nonlinear ARX model, see theidnlarxreference page.

For simulating nonlinear ARX models, you can specify the initial conditions as input/output values, or as a vector. For more information about specifying initial conditions for simulation, see theIDNLARX Modelreference page.

Simulate Nonlinear ARX Model in Simulink

This example uses:

Open Script

This example shows how to compare the simulated output of a Nonlinear ARX Model block to the measured output of a system. You improve the agreement between the measured and simulated responses by estimating initial state values.

Load the sample data and create aniddataobject. Set'Tstart'to 0 so that the data start time matches the Simulink start time 0f 0 s.

loadtwotankdataz = iddata(y,u,0.2,'Tstart',0,'Name','Two tank system'); z1 = z(1:1000);

Estimate a nonlinear ARX model.

mnlarx1 = nlarx(z1,[5 1 3],idWaveletNetwork(8));

你可以now simulate the output of the estimated model in Simulink using the input data inz1。To do so, open a preconfigured Simulink model.

model ='ex_idnlarx_block'; open_system(model);

The model uses the Iddata Source, Nonlinear ARX Model, and Scope blocks. The following block parameters have been preconfigured to specify the estimation data, estimated model, and input and output levels:

Block parameters of Iddata Source block:

  • IDDATA Object-z1

Block parameters of Nonlinear ARX Model block:

  • Model-mnlarx1

  • Initial conditions- Input and output values (default)

  • Input level- 10

  • Output level- 0.1

Run the simulation.

View the difference between measured output and model output by using the Scope block.

simOut = sim(model); open_system([model/范围的])

To improve the agreement between the measured and simulated responses, estimate an initial state vector for the model from the estimation data,z1。

x0 = findstates(mnlarx1,z1,Inf);

集theInitial Conditionsblock parameter value of the Nonlinear ARX Model block toState Values。Specify initial states asx0。

set_param([model'/Nonlinear ARX Model'],'ICspec','State values','X0','x0');

Run the simulation again, and view the difference between measured output and model output in the Scope block. The difference between the measured and simulated responses is now reduced.

simOut = sim(model);

See Also

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