Optimize Parameters for Second-Order DSM in Simulink

Define the performance specifications of a second-order DSM.

outputTable=table();
outputTable.Test=["ACMeas";"ACMeas";"ACMeas";"ACMeas";"ACMeas"];
outputTable.Name={'SNR';'SFDR';'SINAD';'ENOB';'NoiseFloor'};
outputTable.Units={'dB';'dB';'dB';'bits';'dB'};
outputTable.Spec={'> 72';'> 74';'> 72';'maximize 11.5';'< -78'}

outputTable=5×4 table
      Test           Name          Units            Spec       
    ________    ______________    ________    _________________

    "ACMeas"    {'SNR'       }    {'dB'  }    {'> 72'         }
    "ACMeas"    {'SFDR'      }    {'dB'  }    {'> 74'         }
    "ACMeas"    {'SINAD'     }    {'dB'  }    {'> 72'         }
    "ACMeas"    {'ENOB'      }    {'bits'}    {'maximize 11.5'}
    "ACMeas"    {'NoiseFloor'}    {'dB'  }    {'< -78'        }

Define the variables to optimize.

variableTable=table();
variableTable.parameters={'a1';'a2';'b1';'b2'};
variableTable.values=["0.15:0.005:0.16";"0.55:0.005:0.7";"0.15:0.005:0.16";"0.55:0.005:0.7"]

variableTable=4×2 table
    parameters         values      
    __________    _________________

      {'a1'}      "0.15:0.005:0.16"
      {'a2'}      "0.55:0.005:0.7" 
      {'b1'}      "0.15:0.005:0.16"
      {'b2'}      "0.55:0.005:0.7"

Create the msbOptimizer object.

moptimizer = msbOptimizer(SimulationEnvironment='simulink',OutputsSetup=outputTable,VariableSetup=variableTable,DesignName='DSM2ndOrder')

moptimizer = 
  msbOptimizer with properties:

               DesignName: 'DSM2ndOrder'
                   Solver: "surrogateopt"
    SimulationEnvironment: 'simulink'
             BestSolution: []
              BestMetrics: []
     FinalOptimizerStatus: []
             OutputsSetup: [5×4 table]
          ParametersSetup: [4×2 table]
           ParameterNames: ["a1"    "a2"    "b1"    "b2"]
          ParameterValues: ["0.15:0.005:0.16"    "0.55:0.005:0.7"    "0.15:0.005:0.16"    "0.55:0.005:0.7"]
                    Eflag: []
                   Trials: []
              Constraints: [5×9 table]
                  Corners: []

Optimize the parameters.

[sol,metric] = moptimizer.optimizeDesign

Maximum number of simulations: 100
Number of parallel simulations: 1

Figure Optimization Plot Function contains an axes object. The axes object with title DSM2ndOrder Optimization, xlabel Number of simulations, ylabel SNR contains 2 objects of type line. One or more of the lines displays its values using only markers These objects represent Infeasible Best, Best.

Optimizer was able to meet all the specifications.

sol=4×2 table
    Name    Value
    ____    _____

    "a1"     0.15
    "a2"    0.575
    "b1"    0.155
    "b2"    0.635

metric=5×4 table
        Name        FinalMetrics         Specs         Units 
    ____________    ____________    _______________    ______

    "SNR"              72.519       "> 72"             "dB"  
    "SFDR"             80.869       "> 74"             "dB"  
    "SINAD"            72.519       "> 72"             "dB"  
    "ENOB"             11.754       "maximize 11.5"    "bits"
    "NoiseFloor"      -80.755       "< -78"            "dB"

As you can see, the function meets the required specifications. You can use the check point file to continue further optimization from the current state.