Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

an isoperformance set for both bivariate (two design parameters) and multi-variable
design problems.
In this thesis the isoperformance methodology is applied to the hardware tuning
process. Instead of using isoperformance to obtain a design, the methodology aids
in finding a set of uncertainty parameters that, when used in the model, predict the
actual hardware performance. In order to locate the isoperformance set for a system
with two uncertainty parameters and one performance objective, deWeck’s gradient-
based contour following algorithm is employed. This algorithm traces out an isop-
erformance contour using performance gradient information. First, a gradient-based
optimization is used to find an initial point on the contour. In fact, the uncertainty
values obtained through optimized model tuning could be used for this purpose. A
neighboring point on the contour is then found by taking a step in a direction tan-
gential to the contour. The tangent direction is obtained through a singular value
decomposition of the performance gradient with respect to the uncertain parameters.
The contour following algorithm takes advantage of the fact that there are only two
uncertain parameters, but is extensible to the n-dimensional case. The extended algo-
rithm is called tangential front following, and detailed descriptions of it and contour
following are found in [36].
4.3.3 Tuning Algorithm
If the dominant source of uncertainty is parametric and the uncertainty model is
well-known and bounded, then isoperformance can be applied to the model updating
problem to reduce the uncertainty set. The isoperformance tuning algorithm is given
in Figure 4-11 for reference.
To begin, there is one data point from the hardware available, the performance of
the untuned hardware, denoted σ0. Gradient-based contour following is applied over
the uncertainty bounds to obtain the initial isoperformance set, Piso0:
Piso0 = {�p ∈ P |σ (˜x, �y0,�p) =σ0} (4.17)
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