Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

Table 4.1: Tuning parameters for SCI development model.
Name Description y0
m1 -x tuning mass 0 kg
m2 +x tuning mass 0 kg
m1 00 11 000 111
00 11 d1 d2 000 111 d2 00 11
000 111
00 11
000 111
00 11
000 111
m 2
E 1 E 1 E 2 E 2
Y
d1
000 111
000 111
000 111
000 111
000 111
To demonstrate the technique the worst-case uncertainty realizations (∆ = 10%)
of the PT and RPT AO designs are tuned using the SA and SQP algorithms. The
nominal, worst-case and tuned performances are listed in Table 4.2. The worst case
PT performance is significantly higher than nominal, and tuning results in a large
improvement. Tuning also improves the RPT worst-case designs, but the results are
not as dramatic.
Table 4.2: Tuning performance summary for PT and RPT designs.
Algorithm Performance
Performance [µm]
Design nominal worst-case tuned
PT 100.53 1355.5 303.17
RPT AO 263.87 306.86 273.32
In order to evaluate the performance of the different optimization algorithms, tuning
is performed with SA-SQP, MC-SQP and an exhaustive search (ES) of the solution
space. The results for the PT and RPT designs are given in Figures 4-1 and 4-2,
respectively. Each figure includes a table of the algorithm performance data as well
as a two-dimensional surface plot of the tuning solution space. In SA-SQP, a feasible
initial guess is chosen randomly for SA, and the resulting design is used as the initial
guess for the SQP algorithm. In MC-SQP ten feasible initial guesses are randomly
111
X