Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

of these simulations fail to meet requirements all together. In contrast, all of the
simulations meet requirements for both of the RPTT designs, and only 34.5% and
24% of the simulations require tuning for the α =0andα =0.1 designs, respectively.
Since there is a cost associated with hardware tuning it is perferable to design the
system such that it is likely to meet requirements nominally. In this sense, the RPTT
design with α =0.1 performs the best at this requirement and uncertainty level since
all simulations meet the requirement and only 24% require hardware tuning.
A plot of the nominal, worst-case and tuned performances of RPTT designs at
various values of α at this uncertainty level (∆ = 21.5%) is shown in Figure 5-11 along
with the requirment at 330µm. Note that the tuned worst-case performance is above
the requirement for values of α just over 0.1. The results of the hardware simulation
indicate that to produce the best RPTT design α should be set to the maximum value
at which the tuned worst-case performance meets the requirement. This robustness
weighting produces a design that is robust to uncertainty, yet is still tunable to meet
requirements. As indicated by the bar chart (Figure 5-10(b)), including maximum
allowable robustness in the cost increases the number of uncertainty realizations in
which hardware tuning is not necessary.
5.4 Summary
Robust performance tailoring for tuning blends the concepts of robust design and
hardware tuning to produce a design with good nominal performance that is both
robust to uncertainty and has sufficient authority to meet requirements through hard-
ware tuning if necessary. The RPTT formulation extends the idea of robustness from
the design to the physical system by optimizing to facilitate hardware tuning by mini-
mizing the worst-case tuned performance over the uncertainty space. During tailoring,
the performance prediction accounts for the fact that the tuning parameters can be
changed when the hardware is built and the uncertainty is fixed (and known) i.e. the
value of �y is different depending on the value of �p. This knowledge adds extra degrees
of freedom to the problem and results in a less constrained optimization.
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