Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

for tailoring, but tuning parameters are the design variables:
min z
�y,z
(4.12)
s.t. �g(˜x, �y) ≤ 0
hi (z, ˜x, �y, �pi)) ≤ 0 i =1...npv
where the augmented constraints, hi (z, ˜x, �y, �pi), are defined as follows:
hi (z, ˜x, �y, �pi) =−z + f (˜x, �y, �pi) (4.13)
The goal of the AO tuning optimization is to find a set of tuning parameters that
reduce the worst-case model performance over the uncertainty vertices. The resulting
tuning parameters are then used to tune the hardware.
Worst-Case Model Tuning
Another approach that is similar in nature to nominal model tuning and less con-
servative than AO tuning is worst-case model tuning. Instead of tuning the nominal
model to find a tuning configuration, or trying to tune over the entire uncertainty
space, only the model at the worst-case uncertainty vertex is tuned. The worst-case
combination of uncertainty parameters is found by searching over the uncertainty ver-
tices of the untuned model. If the design has been tailored with RPT methods then
this information may already be available. The optimization formulation is similar to
that of nominal model tuning with the worst-case uncertainty values, �pWC, replacing
the nominal values:
min f (˜x, �y, �pWC)
�y
(4.14)
s.t. �g (˜x, �y) ≤ 0
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