Undesirable Parameter Values

What to do if the optimization drives the tuned compensator elements and parameters to undesirable values?

  • When a tuned compensator element or parameter is positive, or when its value is physically constrained to a given range, enter the lower and upper bounds (Minimum and Maximum) in one of the following:

    • Dialog box to select design variables (in Response optimization tool)

    • Compensators pane (in a SISO Design Task)

    This information helps guide the optimization method towards a reasonable solution.

  • Specify initial guesses that are within the range of desirable values.

  • In the Compensators pane in a SISO Design Task, verify that no integrators/differentiators are selected for optimization. Optimizing the pole/zero location of integrators/differentiators can result in drastic changes in the system gain and lead to undesirable values.

What to do if the optimization violates bounds on parameter values?

The Gradient descent optimization method fmincon violates the parameter bounds when it cannot simultaneously satisfy the signal constraints and the bounds. When this happens, try one of the following:

  • Specify a different value for the parameter bound and restart the optimization. A guideline is to adjust the bound by 1% of the typical value.

    For example, for a parameter with a typical value of 1 and lower bound of 0, change the lower bound to 0.01.

  • Relax the signal constraints and restart the optimization. This approach results in a different solution path for the Gradient descent method.

  • Restart the optimization immediately after it terminates by clicking Optimize in the Response Optimization tool. This approach uses the previous optimization results as the starting point for the next optimization cycle to refine the results.

  • Use the following two-step approach to perform the optimization:

    1. Run an initial optimization to satisfy the signal constraints.

      For example, run the optimization using the Simplex search method. This method satisfies the signal constraints but does not support the bounds on parameter values. The results obtained using this method provide the starting point for the optimization performed in the next step. To learn more about this method, see the fminsearch function reference page in the Optimization Toolbox™ documentation.

    2. Reconfigure the optimization by selecting a different optimization method to satisfy both the signal constraints and the parameter bounds.

      For example, change the optimization method to Gradient descent and run the optimization again.

    Tip

    If Global Optimization Toolbox software is installed, you can select the Pattern search optimization method to optimize the model response.