Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

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problem of minimizing a dynamic cost, RMS performance, by varying geometric prop- erties of the structure, cross-sectional diameters and mass distribution, is addressed. The design variables are introduced and practical constraints as well as analytical calculation of cost gradients are discussed. Two well-known optimization algorithms, sequential quadratic programming (SQP) and simulated annealing (SA), are applied to the problem. The results are compared for performance and efficiency and it is found that this particular problem can be solved with a combination of heuristic and gradient methods with reasonable com- putational effort. The PT design obtained through a heuristic SQP approach shows dramatic improvement in performance over the nominal design. The nominal and PT designs are examined in detail to understand why the tailored design produces such a large improvement in performance RMS. 70
Chapter 3 Robust Performance Tailoring “There is nothing so wrong with the analysis as believing the answer!... Uncertainties appear everywhere in the model.. When using a mathematical model, careful attention must be given to the uncertainties in the model.” - Richard Feynman [43] Performance tailoring optimization results in a design that is tailored to achieve a high level of performance given the design variables and constraints. However, in the early design stage, the performance assessment is based only on the model predictions and not on real data. Therefore, there is a question regarding how well the model can predict the actual behavior of the system. In the following chapter this issue of prediction accuracy is explored in detail. First, model uncertainty is defined and methods for quantifying it and assessing the effects of uncertainty on the performance predictions are discussed. Then, parametric uncertainty is identified in the SCI development model and its effect on the performance predictions of the PT design is explored. This study motivates the need for robust design techniques and three existing Robust Performance Tailoring (RPT) cost functions are presented. Results of RPT optimization on the SCI development model are presented and ana- lyzed. The chapter concludes with a discussion of the limitations of robust design on high-performance systems. 71
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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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Page 39 and 40: Chapter 2 Performance Tailoring A c
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Page 51 and 52: 2.3.3 Design Variables The choice o
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Page 91 and 92: Statistical Robustness The statisti
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Page 97 and 98: Norm. Cum. Var. [µm 2 ] PSD [µm 2
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Page 107 and 108: Chapter 4 Dynamic Tuning Robust Per
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Performance Requirement [µm] 400 3
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is considered. 4.2.1 Hardware-only
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and added to the objective function
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using either a decreasing step-size
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for tailoring, but tuning parameter
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tained by randomly choosing paramet
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p [GPa] y ∗ [kg] Performance [µm
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# Func. Evals Performance RMS (µm)
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tion changes in the updated solutio
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Data: initial iterate, p0, performa
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the new tuning configuration is ver
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Performing an AO tuning optimizatio
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Uncertainty Bounds Test �y [kg] S
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Table 4.6: Tuning results on fifty
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eters are discussed. The optimizati
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Chapter 5 Robust Performance Tailor
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MPC optimization by allowing a diff
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where the notation yij indicates th
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(Table 4.1), and the uncertainty pa
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Table 5.2: Performance and design p
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it in the worst-case uncertainty re
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The data in Figure 5-2 indicate tha
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configuration. The tuned configurat
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same requirement. The effect become
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indicating that this requirement is
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E 2 [Pa] 7.8 7.6 7.4 7.2 7 6.8 6.6
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than the RPT design, 155.45µm to 5
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have a very small nominal performan
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of these simulations fail to meet r
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and that it is the only design meth
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Chapter 6 Focus Application: Struct
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optical path differences between th
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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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Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

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