Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT

trade between design flexibility and prediction accuracy.
First the idea of structural optimization is introduced through a design method
termed Performance Tailoring (PT) (Chapter 2). The objective of a PT optimization
is to find a set of design parameters that minimize the root mean square (RMS) of
the performance metric. A simple model representative of a structurally-connected
interferometer (SCI) is introduced as the development model and is described in de-
tail. Methods for evaluating the RMS performance of the structure when subject
to a white noise disturbance environment are discussed. Two types of optimization
algorithms, a constrained gradient-based search known as sequential quadratic pro-
gramming (SQP), and a popular heuristic search called simulated annealing (SA) are
reviewed. These algorithms are applied to the problem of performance tailoring the
development model to minimize the variance of the optical path length difference
between the two interferometer arms. The algorithms are compared for performance
and efficiency, and the physical mechanisms at work in the tailored design are ex-
plored. It is shown that the nominal output RMS can be reduced to one quarter of
its value by tailoring the truss diameters such that the truss is very soft and most
of the mass is in the collector optics at the ends of the arrays, and the optics are
effectively isolated from the disturbance at the array center.
In Chapter 3 the concept of model uncertainty is introduced, and a brief review
of model forms and analysis techniques is provided. Uncertainty is added to the
development model through the Young’s Modulus of the truss segments. An uncer-
tainty analysis is conducted and it is shown that the RMS performance of the PT
design degrades dramatically when asymmetry is introduced to the model through
the uncertainty parameters. The PT design only performs well at the nominal un-
certainty values and covers a large range of performance predictions when the effects
of model uncertainty are considered. To address the problem Robust Performance
Tailoring (RPT), a subset of the robust design field, is applied. RPT is an design
optimization in which the effects of the uncertainty are considered in the objective
function through a robustness metric. Three well-developed robust cost functions
are considered: anti-optimization, multiple model and a technique that incorporates
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