sdo.getParameterFromModel

Load a Simulink model.

load_system("sldo_model1_stepblk")

If you do not specify any model parameters, sdo.getParameterFromModel finds all the tunable parameters in the model and treats them as continuous parameters.

DesignVars = sdo.getParameterFromModel("sldo_model1_stepblk");
DesignVars

 
DesignVars(1,1) =
 
       Name: 'Kd'
      Value: 0
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 1
       Info: [1x1 struct]

 
DesignVars(2,1) =
 
       Name: 'Ki'
      Value: 0
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 1
       Info: [1x1 struct]

 
DesignVars(3,1) =
 
       Name: 'Kp'
      Value: 1
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 1
       Info: [1x1 struct]

 
DesignVars(4,1) =
 
       Name: 'w0'
      Value: 0.5000
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 0.5000
       Info: [1x1 struct]

 
DesignVars(5,1) =
 
       Name: 'zeta'
      Value: 0.5000
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 0.5000
       Info: [1x1 struct]

 
5x1 param.Continuous
 

Thus, for this model, which has tunable parameters Kd, Ki, Kp, w0, and zeta, the output DesignVars is an array of five param.Continuous objects. To prepare the parameters for optimization, you can set the initial value, minimum and maximum values, and free elements of each parameter as needed. For instance, set the minimum value of Kd to 0.001, the maximum value to 100, and the initial value to 1.

DesignVars(1).Minimum = 1e-3;
DesginVars(1).Maximum = 100;
DesignVars(1).Value = 1;

Instead of designating all tunable model parameters as design variables for optimization, you can specify a subset of variables. Load a Simulink model that has five tunable parameters: Kp, Ki, Kd, w0, and zeta. Configure Kp, Ki, and Kd as continuous design variables.

mdl = "sldo_model1_stepblk";
open_system(mdl)

continParams = ["Ki","Kp","Kd"];
DesignVars = sdo.getParameterFromModel(mdl,continParams)

 
DesignVars(1,1) =
 
       Name: 'Ki'
      Value: 0
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 1
       Info: [1x1 struct]

 
DesignVars(2,1) =
 
       Name: 'Kp'
      Value: 1
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 1
       Info: [1x1 struct]

 
DesignVars(3,1) =
 
       Name: 'Kd'
      Value: 0
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 1
       Info: [1x1 struct]

 
3x1 param.Continuous
 

DesignVars is a vector of three param.Continuous objects corresponding to Ki, Kp, and Kd in the order you specified them. Suppose that you also want to tune w0 and zeta, but you want to restrict them to a finite, discrete set of values. To do so, specify these parameters as the third input argument to sdo.getParameterFromModel.

discreteParams = ["w0","zeta"];
DesignVars = sdo.getParameterFromModel(mdl,continParams,discreteParams);

Now DesignVars is a vector of length five, containing three param.Continuous objects and two param.Discrete objects. For instance, compare the third entry, Ki, with the fourth entry, corresponding to w0.

DesignVars(3)

 
ans =
 
       Name: 'Kd'
      Value: 0
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 1
       Info: [1x1 struct]

 
1x1 param.Continuous
 

DesignVars(4)

 
ans =
 
        Name: 'w0'
       Value: 0.5000
    ValueSet: 0.5000
        Free: 1
        Info: [1x1 struct]

 
1x1 param.Discrete
 

The Value properties of the parameters are initialized to their values in the model. To continue with parameter estimation or response optimization, configure the initial values, the Minimum and Maximum properties of the continuous variables, and the ValueSet properties of the discrete parameters as needed. Note that to optimize discrete parameters, you must set the optimization method of sdo.optimize to surrogateopt. This method requires that all continuous parameters have finite minimum and maximum values.

If you want to designate all model parameters as discrete-valued, you can use the placeholder "*" as an input to sdo.getParameterFromModel to avoid explicitly listing every model parameter in the discreteParams argument. Similarly, if you specify a subset of model parameters in continParams, you can use "*" to specify the remaining parameters as discrete-valued.

Load a model and designate all model parameters as discrete-valued. To do so, specify [] to indicate that there are no continuous parameters, and "*" to indicate that all parameters are discrete.

mdl = "sldo_model1_stepblk";
load_system(mdl)
 
DesignVars = sdo.getParameterFromModel(mdl,[],"*");
size(DesignVars)

ans = 1×2

     5     1

DesignVars has five entries, corresponding to the five tunable parameters in the model, Kd, Ki, Kp, zeta, and w0. All parameters are represented by param.Discrete parameter objects. For instance, examine the third parameter.

DesignVars(2)

 
ans =
 
        Name: 'Ki'
       Value: 0
    ValueSet: 0
        Free: 1
        Info: [1x1 struct]

 
1x1 param.Discrete
 

Now, suppose that only w0 is continuous-valued, while you want to limit all remaining parameters to discrete values.

DesignVars = sdo.getParameterFromModel(mdl,"w0","*");

DesignVars still contains five entries, but this time the first entry is a param.Continuous parameter object.

DesignVars(1)

 
ans =
 
       Name: 'w0'
      Value: 0.5000
    Minimum: -Inf
    Maximum: Inf
       Free: 1
      Scale: 0.5000
       Info: [1x1 struct]

 
1x1 param.Continuous
 

The remaining entries are discrete parameters.