Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

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model updating [60, 55, 12] and is addressed in a future section. Hardware Tuning The hardware simulation is tuned in real time with the barrier steepest descent algorithm (BSD) and simulated annealing (SA). The tuning configuration from the model-only method that resulted in the best performance (in this case, nominal model tuning) is used as the starting point. The resulting tuning parameters, tuned performance and required number of hardware tests are listed in the table in Figure 4-10. Although each method resulted in a different tuning mass configuration, both suc- ceeded in bringing performance below the requirement. However, in order to achieve this goal a large number of tests, more than 2000 in the case of SA, are required. The results are shown graphically in the lower figure, Figure 4-10(b). The bar chart shows the number of function evaluations required for each method. In this example, the barrier method found a working configuration much more quickly than simulated annealing. Simulated annealing takes a long time since it is a random search and is not directed by gradient information. The lower subplot shows the performance results obtained by each algorithm. The solid line is the requirement, and the dotted lines indicates the nominal hardware performances. It is clear that both algorithms succeed in meeting the requirement. 4.3 Isoperformance Updating for Tuning In the previous section tuning methods ranging from those that use only the model to others that reject the model and tune directly on hardware are explored. It is shown that a trade exists between cost and reliability. The model-only methods are low- cost since only one hardware test is necessary. The tuning optimization is done using only the model and the resulting tuning parameters are then applied to the hardware. However, since these methods rely only on a model that is uncertain and consequently not exact they do not always succeed in tuning the hardware within performance. In fact, for the hardware simulation considered, the tuned hardware performance is 134
# Func. Evals Performance RMS (µm) 2000 1000 0 295 290 285 280 275 y ∗ [kg] Performance # m1 m2 [µm] Tests BSD 0.0 10.40 278.87 61 SA 0.09 12.42 277.24 2176 pre−tuned HW requirement (a) 1 2 BSD SA (b) Figure 4-10: Results of Hardware tuning algorithms: (a) table of results (b) number of tests required and tuned performances, σHW0 = 290.83µm (dashed line), σreq = 280µm (solid line). 135
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Dynamic Tailoring and Tuning for Sp
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Acknowledgments This work was suppo
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3.2 RPT Formulation . . . . . . . .
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List of Figures 1-1 Timeline of Ori
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List of Tables 1.1 Effect of simula
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Nomenclature Abbreviations ACS atti
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dk optimization search direction f
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1.1 Space-Based Interferometry NASA
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unfettered by the Earth’s atmosph
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the SCI, both the size and flexibil
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maybethatitbecomescertain that the
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Table 1.1: Effect of simulation res
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Table 1.2: Effect of simulation res
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has been found that structural desi
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precision telescope structure for m
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attractive, and more conservative a
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to solve the performance tailoring
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Chapter 2 Performance Tailoring A c
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ometer (SCI). In the following sect
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The equations of motion of the unda
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The frequency response functions fr
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the output covariance matrix, Σz,
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where the subscript indicates the i
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2.3.3 Design Variables The choice o
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and then, by inspection, the inerti
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algorithms begin at an initial gues
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at least locally optimal, and the s
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initial design variable state, x =
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and the RMS OPD is computed using E
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# Designs 25 20 15 10 5 Accepted, b
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does not provide information on why
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energy is distributed almost evenly
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also symmetric as seen in the figur
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Chapter 3 Robust Performance Tailor
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through careful and experienced mod
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described above. However, one can r
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ic, σz(�x, �p), that is depend
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Magnitude, OPD/F x [µm/N] Magnitud
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% Energy 100 90 80 70 60 50 40 30 2
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Page 91 and 92: Statistical Robustness The statisti
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Page 95 and 96: (Figure 3-6(b)). The nominal perfor
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Page 119 and 120: Norm. Cum. Var. [µm 2 ] PSD [µm 2
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Page 133: p [GPa] y ∗ [kg] Performance [µm
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Table 6.1: RWA disturbance model pa
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FRF Magnitude 10 1 10 0 10 −1 10
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Y Z Z X Y (a) w (c) w Y h Z Figure
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Table 6.6: Primary mirror propertie
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OP1 STAR Z OP2 OP3 Coll 1 Coll 2 Bu
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PSD OPD14 [m 2 /Hz] CumulativeOPD14
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6.2 Design Parameters In order to a
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6.2.2 Tuning The tuning parameters
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complex and the normal modes analys
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does not change with the design par
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(a) (b) (c) Figure 6-11: SCI TPF PT
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Table 6.14: Performance predictions
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performance trends similar to those
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Chapter 7 Conclusions and Recommend
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a statistical robustness measure su
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and the worst-case performance is a
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- Consider uncertainty analysis too
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Appendix A Gradient-Based Optimizat
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such that the gradient direction is
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the descent direction. In some case
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Bibliography [1] Jpl planet quest w
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[24] Nightsky Systems Carl Blaurock
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[48] S. C. O. Grocott, J. P. How, a
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[74] M. Lieber. Development of ball
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AIAA/ASME/ASCE/AHS/ASC Structures,
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