T contains the same tunable components as
the input closed-loop model
T0. However, the parameter
T are now tuned to minimize the H∞ norm
of this transfer function.
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,
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
is a local minimum of the gain minimization problem. For best results,
RandomStart option to
obtain several minimization runs. Setting
N > 0 causes
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.