Tunable Second-Order Filter

This example shows how to create a parametric model of the second-order filter:


where the damping ζ and the natural frequency ωn are tunable parameters.

  1. Define the tunable parameters using realp.

    wn = realp('wn',3);
    zeta = realp('zeta',0.8);

    wn and zeta are realp parameter objects, with initial values 3 and 0.8, respectively.

  2. Create a model of the filter using the tunable parameters.

    F = tf(wn^2,[1 2*zeta*wn wn^2])

    The inputs to tf are the vectors of numerator and denominator coefficients expressed in terms of wn and zeta.

    F is a genss. The property F.Blocks lists the two tunable parameters wn and zeta.

  3. (Optional) Examine the number of tunable blocks in the model using nblocks.


    This command returns the result:

    ans =

    F has two tunable parameters, but the parameter wn appears five times — twice in the numerator and three times in the denominator.

  4. (Optional) Rewrite F for fewer occurrences of wn.

    The second-order filter transfer function can be expressed as follows:


    Use this expression to create the tunable filter:

    F = tf(1,[(1/wn)^2 2*zeta*(1/wn) 1])
  5. (Optional) Examine the number of tunable blocks in the new filter model.


    This command returns the result:

    ans =

    In the new formulation, there are only three occurrences of the tunable parameter wn. Reducing the number of occurrences of a block in a model can improve performance time of calculations involving the model. However, the number of occurrences does not affect the results of tuning the model or sampling it for parameter studies.

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