Interpret the Outputs of hinfstruct

Output Model is Tuned Version of Input Model

T contains the same tunable components as the input closed-loop model T0. However, the parameter values of T are now tuned to minimize the H_∞ norm of this transfer function.

Interpreting gamma

gamma is the smallest H_∞ norm achieved by the optimizer. Examine gamma to determine how close the tuned system is to meeting your design constraints. If you normalize your H_∞ constraints, a final gamma value of 1 or less indicates that the constraints are met. A final gamma value exceeding 1 by a small amount indicates that the constraints are nearly met.

The value of gamma that hinfstruct returns is a local minimum of the gain minimization problem. For best results, use the RandomStart option to hinfstruct to obtain several minimization runs. Setting RandomStart to an integer N > 0 causes hinfstruct to run the optimization N additional times, beginning from parameter values it chooses randomly. For example:

opts = hinfstructOptions('RandomStart',5);
[T,gamma,info] = hinfstruct(T0,opts);

You can examine gamma for each run to identify an optimization result that meets your design requirements.

For more details about hinfstruct, its options, and its outputs, see the hinfstruct and hinfstructOptions reference pages.