Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT
Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT
Performance [µm] 600 550 500 450 400 350 300 250 Nominal Worst−Case Tuned 200 0 0.2 0.4 0.6 0.8 1 Weighting, α Figure 5-11: RPTT design performance as a function of weighting (α)for ∆ = 21.5%: (–) requirement. The RPTT formulations are applied to the SCI development model with uncer- tainty bounds of ∆ = 10% using SA and SQP optimization algorithms. The results are compared for performance and efficiency. It is found that a blend of SA and SQP in which the SA design is used as an initial guess for SQP performs best. In addition, a simple minimization form of the RPTT cost function in which the tuned and nominal performances at the uncertainty vertices are included in the constraints is found to be the best posed for SQP optimization. The nominal, worst-case and tuned performances of the optimal RPTT design are compared to those of the PT and RPT designs. It is found that the RPTT design is somewhere between the PT and RPT designs in terms of nominal performance and robustness. It has a better nominal performance than the RPT design, but is more sensitive to uncertainty. However, the tuned worst-case performance of the RPTT design is better than those of either the PT or RPT designs. A design regimes plot shows that the RPTT design further extends the design space for this model allowing more aggressive performance requirements to be met at higher levels of uncertainty 178
and that it is the only design method that is successful in all of the reachable regimes. Finally, a set of randomly generated hardware simulations are run for two different levels of uncertainty and performance requirement. Simulations of the RPT designs are never successful because too much focus is placed on insensitivity to uncertainty. A good number of the simulated PT designs are successful, but there are some that fail. In addition, much dependance is placed on tuning as few of the nominal hardware configurations meet the requirement. In contrast, all simulations of the RPTT design succeed and the majority do not require tuning. 179
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and that it is the only design method that is successful in all of the reachable regimes.<br />
Finally, a set of randomly generated hardware simulations are run for two different<br />
levels of uncertainty and performance requirement. Simulations of the RPT designs<br />
are never successful because too much focus is placed on insensitivity to uncertainty.<br />
A good number of the simulated PT designs are successful, but there are some that<br />
fail. In addition, much dependance is placed on tuning as few of the nominal hardware<br />
configurations meet the requirement. In contrast, all simulations of the RPTT design<br />
succeed and the majority do not require tuning.<br />
179
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