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
(a) (b) (c) Figure 6-13: SCI TPF RPTT design (a) top view (XY) (b) front view (XZ) (c) isometric view. Performance: RMS OPD [nm] 14 12 10 8 6 4 2 Nominal WC tuned PT RPT RPTT Figure 6-14: Nominal, worst-case and tuned performances for all TPF SCI designs. 206
Table 6.14: Performance predictions for all TPF SCI designs. RMS OPD14 [nm] σ0 σWC σt PT 2.90 12.70 9.01 RPT 3.79 8.38 4.95 RPTT 5.99 13.15 4.28 Each design is tuned with the parameters in Table 6.11 at the worst-case un- certainty vertices using Equation 4.2 and SA. This step serves as a simulation of hardware tuning, and the resulting parameter values are listed in Table 6.15. The tuned RMS performances are listed in Table 6.15 and shown graphically in Figure 6-14 with triangles and dotted error bars. As expected, the best tuned performance (4.28 nm) is achieved by the RPTT design. The PT design does not appear to be very tunable as the worst-case performance only improves from 12.8 nm to 9 nm. The RPT design exhibits more favorable tuning properties and can be adjusted to 4.95 nm from the worst-case performance of 8.4 nm. While the overall trend observed is similar to that in the development model (Figure 5-1), it is interesting to note that in the TPF SCI model the RPT design is more tunable than the PT design. In this problem, robustness to uncertainty and tunability are not coupled as they are in the development problem. As a result, the RPTT system is similar in design to the RPT system. However, it still true that explicitly tailoring for tuning improves the performance of the tuned system. Table 6.15: Tuning parameters for tuned worst-case TPF SCI realizations. rPM fiso PT 0.07 7.53 RPT 0.213 6.96 RPTT 0.246 6.41 Although the results in Figure 6-14 follow the general trends expected for the PT, RPT and RPTT systems, the design improvement effected by RPTT is not as dramatic as in the development model. One explanation is that the optimization is limited by the lack of gradient information and computational inefficiencies. Recall 207
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Table 6.14: <strong>Performance</strong> predictions for all TPF SCI designs.<br />
RMS OPD14 [nm]<br />
σ0 σWC σt<br />
PT 2.90 12.70 9.01<br />
RPT 3.79 8.38 4.95<br />
RPTT 5.99 13.15 4.28<br />
Each design is tuned <strong>with</strong> the parameters in Table 6.11 at the worst-case un-<br />
certainty vertices using Equation 4.2 and SA. This step serves as a simulation of<br />
hardware tuning, and the resulting parameter values are listed in Table 6.15. The<br />
tuned RMS performances are listed in Table 6.15 and shown graphically in Figure 6-14<br />
<strong>with</strong> triangles and dotted error bars. As expected, the best tuned performance (4.28<br />
nm) is achieved by the RPTT design. The PT design does not appear to be very<br />
tunable as the worst-case performance only improves from 12.8 nm to 9 nm. The<br />
RPT design exhibits more favorable tuning properties and can be adjusted to 4.95<br />
nm from the worst-case performance of 8.4 nm. While the overall trend observed<br />
is similar to that in the development model (Figure 5-1), it is interesting to note<br />
that in the TPF SCI model the RPT design is more tunable than the PT design. In<br />
this problem, robustness to uncertainty and tunability are not coupled as they are in<br />
the development problem. As a result, the RPTT system is similar in design to the<br />
RPT system. However, it still true that explicitly tailoring for tuning improves the<br />
performance of the tuned system.<br />
Table 6.15: <strong>Tuning</strong> parameters for tuned worst-case TPF SCI realizations.<br />
rPM fiso<br />
PT 0.07 7.53<br />
RPT 0.213 6.96<br />
RPTT 0.246 6.41<br />
Although the results in Figure 6-14 follow the general trends expected for the<br />
PT, RPT and RPTT systems, the design improvement effected by RPTT is not as<br />
dramatic as in the development model. One explanation is that the optimization is<br />
limited by the lack of gradient information and computational inefficiencies. Recall<br />
207