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Vary Uncertainty Values Across Multiple Uncertain State Space Blocks

This example shows the workflow for varying uncertainty values across multiple Uncertain State Space blocks in a Simulink® model. Use this approach for complex models with large number of uncertain variables or Uncertain State Space blocks.

This section uses a Simulink model to provide step-by-step instructions for toggling between nominal and user-defined uncertainty values at the MATLAB® prompt.

  1. Open the Simulink model rct_sim_ex2.

    rct_sim_ex2

    The model contains two Uncertain State Space blocks, as shown in the following figure.

    The Unmodeled dynamics and First order with uncertain pole blocks depend on the uncertain variables input_unc and a.

  2. Double-click the Unmodeled dynamics block to open the block parameters dialog box. The Uncertainty value field contains the variable val_all. Similarly, the Uncertainty value field in the First order with uncertain pole block parameters dialog contains the variable val_all. You use this variable to vary the uncertain variable values across both the Uncertain State Space blocks.

      Note   When defining val_all, you can enter only a subset of uncertain variables referenced by the model in the structure. When you do not specify some uncertain variables, the software uses their nominal value during simulation.

  3. At the MATLAB prompt, specify val_all = []; and click

    to simulate the model.

    The software uses the nominal values of the uncertain variables a and input_unc during simulation. After the simulation completes, the MultiPlot Graph block shows the following figure.

  4. Generate random samples of uncertainty values:

    1. Find all Uncertain State Space blocks and associated uncertain variables in the model.

      uvars=ufind('rct_sim_ex2')

      MATLAB returns the following result:

      uvars =

                a: [1x1 ureal]
        input_unc: [1x1 ultidyn]
      

      The uncertain variables a and input_unc are ureal and ultidyn objects, respectively and the structure uvars lists them by name.

    2. Randomly sample the uncertain variables.

      val_all = usample(uvars)

      MATLAB returns the following result:

      val_all =

                a: -1.1167
        input_unc: [1x1 ss]
      

      The structure val_all contains sample values of the uncertain variables a and input_unc. The software samples the values within the specified uncertainty ranges for a and input_unc.

  5. Simulate the model for the uncertainty values val_all. By repeating the process inside a for-loop, you can assess how uncertainty affects the model responses. For example, perform 10 simulations using random uncertainty values:

    for i=1:10;
           val_all = usample(uvars)
           sim('rct_sim_ex2',10);
    end

During each simulation, the software samples values of the uncertain variables input_unc and a and plots the response for the sampled values. The MultiPlot Graph block shows the following responses obtained using random sample values of uncertain variables.

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