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Estimate Model Parameter Values (Code)

This example shows how to use experimental data to estimate model parameter values.

Aircraft Model

The Simulink model, sdoAircraftEstimation, models the longitudinal flight control system of an aircraft.

open_system('sdoAircraftEstimation')

Estimation Problem

You use measured data to estimate the aircraft model parameters and states.

Measured output data:

  • Pilot G force, output of the Pilot G-force calculation block

  • Angle of attack, fourth output of the Aircraft Dynamics Model block

Parameters:

  • Actuator time constant, Ta, used by the Actuator Model block

  • Vertical velocity, Zd, used by the Aircraft Dynamics Model block

  • Pitch rate gains, Md, used by the Aircraft Dynamics Model block

State:

  • Initial state of the first-order actuator model, sdoAircraftEstimation/Actuator Model

Define the Estimation Experiment

Get the measured data.

[time,iodata] = sdoAircraftEstimation_Experiment;

The sdoAircraftEstimation_Experiment function returns the measured output data, iodata, and the corresponding time vector. The first column of iodata is the pilot G force and the second column is the angle of attack.

To see the code for this function, type edit sdoAircraftEstimation_Experiment.

Create an experiment object to store the measured input/output data.

Exp = sdo.Experiment('sdoAircraftEstimation');

Create an object to store the measured pilot G-Force output.

PilotG = Simulink.SimulationData.Signal;
PilotG.Name      = 'PilotG';
PilotG.BlockPath = 'sdoAircraftEstimation/Pilot G-force calculation';
PilotG.PortType  = 'outport';
PilotG.PortIndex = 1;
PilotG.Values    = timeseries(iodata(:,2),time);

Create an object to store the measured angle of attack (alpha) output.

AoA = Simulink.SimulationData.Signal;
AoA.Name = 'AngleOfAttack';
AoA.BlockPath = 'sdoAircraftEstimation/Aircraft Dynamics Model';
AoA.PortType  = 'outport';
AoA.PortIndex = 4;
AoA.Values    = timeseries(iodata(:,1),time);

Add the measured pilot G-Force and angle of attack data to the experiment as the expected output data.

Exp.OutputData = [...
    PilotG; ...
    AoA];

Add the initial state for the Actuator Model block to the experiment. Set its Free field to true so that it is estimated.

Exp.InitialStates = sdo.getStateFromModel('sdoAircraftEstimation','Actuator Model');
Exp.InitialStates.Minimum = 0;
Exp.InitialStates.Free    = true;

Compare the Measured Output and the Initial Simulated Output

Create a simulation scenario using the experiment and obtain the simulated output.

Simulator    = createSimulator(Exp);
Simulator    = sim(Simulator);

Search for the pilot G-Force and angle of attack signals in the logged simulation data.

SimLog       = find(Simulator.LoggedData,get_param('sdoAircraftEstimation','SignalLoggingName'));
PilotGSignal = find(SimLog,'PilotG');
AoASignal    = find(SimLog,'AngleOfAttack');

Plot the measured and simulated data.

As expected, the model response does not match the experimental output data.

plot(time, iodata, ...
    AoASignal.Values.Time,AoASignal.Values.Data,'--', ...
    PilotGSignal.Values.Time,PilotGSignal.Values.Data,'-.');
title('Simulated and Measured Responses Before Estimation')
legend('Measured angle of attack',  'Measured pilot g force', ...
     'Simulated angle of attack', 'Simulated pilot g force');

Specify the Parameters to Estimate

Select the model parameters that describe the flight control actuation system. Specify bounds for the estimated parameter values based on our understanding of the actuation system.

p = sdo.getParameterFromModel('sdoAircraftEstimation',{'Ta','Md','Zd'});
p(1).Minimum = 0.01;  %Ta
p(1).Maximum = 1;
p(2).Minimum = -10;   %Md
p(2).Maximum = 0;
p(3).Minimum = -100;  %Zd
p(3).Maximum = 0;

Get the actuator initial state value that is to be estimated from the experiment.

s = getValuesToEstimate(Exp);

Group the model parameters and initial states to be estimated together.

v = [p;s]
 
v(1,1) =
 
       Name: 'Ta'
      Value: 0.5000
    Minimum: 0.0100
    Maximum: 1
       Free: 1
      Scale: 0.5000
       Info: [1x1 struct]

 
v(2,1) =
 
       Name: 'Md'
      Value: -1
    Minimum: -10
    Maximum: 0
       Free: 1
      Scale: 1
       Info: [1x1 struct]

 
v(3,1) =
 
       Name: 'Zd'
      Value: -80
    Minimum: -100
    Maximum: 0
       Free: 1
      Scale: 128
       Info: [1x1 struct]

 
v(4,1) =
 
       Name: 'sdoAircraftEstimation/Actuator
Model'
      Value: 0
    Minimum: 0
    Maximum: Inf
       Free: 1
      Scale: 1
    dxValue: 0
     dxFree: 1
       Info: [1x1 struct]

 
4x1 param.Continuous
 

Define the Estimation Objective Function

Create an estimation objective function to evaluate how closely the simulation output, generated using the estimated parameter values, matches the measured data.

Use an anonymous function with one input argument that calls the sdoAircraftEstimation_Objective function. We pass the anonymous function to sdo.optimize, which evaluates the function at each optimization iteration.

estFcn = @(v) sdoAircraftEstimation_Objective(v,Exp);

The sdoAircraftEstimation_Objective function:

  • Has one input argument that specifies the actuator parameter values and the actuator initial state.

  • Has one input argument that specifies the experiment object containing the measured data.

  • Returns a vector of errors between simulated and experimental outputs.

The sdoAircraftEstimation_Objective function requires two inputs, but sdo.optimize requires a function with one input argument. To work around this, estFcn is an anonymous function with one input argument, v, but it calls sdoAircraftEstimation_Objective using two input arguments, v and Exp.

For more information regarding anonymous functions, see "Anonymous Functions""Anonymous Functions".

The sdo.optimize command minimizes the return argument of the anonymous function estFcn, that is, the residual errors returned by sdoAircraftEstimation_Objective. For more details on how to write an objective/constraint function to use with the sdo.optimize command, type help sdoExampleCostFunction at the MATLAB command prompt.

To examine the estimation objective function in more detail, type edit sdoAircraftEstimation_Objective at the MATLAB command prompt.

type sdoAircraftEstimation_Objective
function vals = sdoAircraftEstimation_Objective(v,Exp) 
%SDOAIRCRAFTESTIMATION_OBJECTIVE
%
%    The sdoAircraftEstimation_Objective function is used to compare model
%    outputs against experimental data.
%
%    vals = sdoAircraftEstimation_Objective(v,Exp) 
%
%    The |v| input argument is a vector of estimated model parameter values
%    and initial states.
%
%    The |Exp| input argument contains the estimation experiment data.
%
%    The |vals| return argument contains information about how well the
%    model simulation results match the experimental data and is used by
%    the |sdo.optimize| function to estimate the model parameters.
%
%    See also sdo.optimize, sdoExampleCostFunction,
%    sdoAircraftEstimation_cmddemo
%
 
% Copyright 2012 The MathWorks, Inc.

%%
% Define a signal tracking requirement to compute how well the model output
% matches the experiment data. Configure the tracking requirement so that
% it returns the tracking error residuals (rather than the
% sum-squared-error) and does not normalize the errors.
%
r = sdo.requirements.SignalTracking;
r.Type      = '==';
r.Method    = 'Residuals';
r.Normalize = 'off';

%%
% Update the experiments with the estimated parameter values.
%
Exp  = setEstimatedValues(Exp,v);

%%
% Simulate the model and compare model outputs with measured experiment
% data.
%
Simulator = createSimulator(Exp);
Simulator = sim(Simulator);

SimLog       = find(Simulator.LoggedData,get_param('sdoAircraftEstimation','SignalLoggingName'));
PilotGSignal = find(SimLog,'PilotG');
AoASignal    = find(SimLog,'AngleOfAttack');

PilotGError = evalRequirement(r,PilotGSignal.Values,Exp.OutputData(1).Values);
AoAError    = evalRequirement(r,AoASignal.Values,Exp.OutputData(2).Values);

%%
% Return the residual errors to the optimization solver.
%
vals.F = [PilotGError(:); AoAError(:)];
end

Estimate the Parameters

Use the sdo.optimize function to estimate the actuator parameter values and initial state.

Specify the optimization options. The estimation function sdoAircraftEstimation_Objective returns the error residuals between simulated and experimental data and does not include any constraints, making this problem ideal for the 'lsqnonlin' solver.

opt = sdo.OptimizeOptions;
opt.Method = 'lsqnonlin';

Estimate the parameters.

vOpt = sdo.optimize(estFcn,v,opt)
 Optimization started 04-Sep-2014 05:12:07

                               Step-size  First-order 
 Iter F-count        f(x)                 optimality
    0      9      27972.2            1                                         
    1     18      10124.8       0.4744     5.69e+04
    2     27      3127.92       0.3854     1.24e+04
    3     36      872.751       0.4286     2.81e+03
    4     45       238.66       0.5147          618
    5     54      71.9182        0.493          147
    6     63      17.2194       0.4163         44.9
    7     72      1.82827       0.3069         11.4
    8     81    0.0440753       0.1321         1.38
    9     90    0.0020167      0.03129       0.0871
   10     99  0.000244535     0.008145        0.119
   11    108  5.74974e-05     0.005686      0.00522
Local minimum possible.

lsqnonlin stopped because the final change in the sum of squares relative to 
its initial value is less than the selected value of the function tolerance.
 
vOpt(1,1) =
 
       Name: 'Ta'
      Value: 0.0500
    Minimum: 0.0100
    Maximum: 1
       Free: 1
      Scale: 0.5000
       Info: [1x1 struct]

 
vOpt(2,1) =
 
       Name: 'Md'
      Value: -6.8848
    Minimum: -10
    Maximum: 0
       Free: 1
      Scale: 1
       Info: [1x1 struct]

 
vOpt(3,1) =
 
       Name: 'Zd'
      Value: -63.9983
    Minimum: -100
    Maximum: 0
       Free: 1
      Scale: 128
       Info: [1x1 struct]

 
vOpt(4,1) =
 
       Name: 'sdoAircraftEstimation/Actuator
Model'
      Value: 6.3839e-05
    Minimum: 0
    Maximum: Inf
       Free: 1
      Scale: 1
    dxValue: 0
     dxFree: 1
       Info: [1x1 struct]

 
4x1 param.Continuous
 

Compare the Measured Output and the Final Simulated Output

Update the experiments with the estimated parameter values.

Exp = setEstimatedValues(Exp,vOpt);

Simulate the model using the updated experiment and compare the simulated output with the experimental data.

The model response using the estimated parameter values closely matches the experiment output data.

Simulator    = createSimulator(Exp);
Simulator    = sim(Simulator);
SimLog       = find(Simulator.LoggedData,get_param('sdoAircraftEstimation','SignalLoggingName'));
PilotGSignal = find(SimLog,'PilotG');
AoASignal    = find(SimLog,'AngleOfAttack');

plot(time, iodata, ...
    AoASignal.Values.Time,AoASignal.Values.Data,'-.', ...
    PilotGSignal.Values.Time,PilotGSignal.Values.Data,'--')
title('Simulated and Measured Responses After Estimation')
legend('Measured angle of attack',  'Measured pilot g force', ...
    'Simulated angle of attack', 'Simulated pilot g force');

Update the Model Parameter Values

Update the model with the estimated actuator parameter values. Do not update the model actuator initial state (fourth element of vOpt) as it is dependent on the experiment.

sdo.setValueInModel('sdoAircraftEstimation',vOpt(1:3));

Related Examples

To learn how to estimate model parameters using the Parameter Estimation Tool, see "Estimate Model Parameter Values (GUI)".

Close the model

bdclose('sdoAircraftEstimation')
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