Requirements management lessons learned: fuzzy "most likely" versus clean shaven "not to exceed"

Paul A. Lightsey

doi:10.1117/12.2196564

9 September 2015 Requirements management lessons learned: fuzzy "most likely" versus clean shaven "not to exceed"

Paul A. Lightsey

Proceedings Volume 9583, An Optical Believe It or Not: Key Lessons Learned IV; 95830B (2015) https://doi.org/10.1117/12.2196564
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

Mission objectives often have a level of imprecision that lends itself to a fuzzy logic approach, even though the more traditional approach is to flow down bimodal pass/fail “not to exceed” type of requirements. Examples will be given for large astronomical telescope applications involving optical performance, active wave front and control, and radiometric/stray light controls demonstrating the pros and cons of the two contrasting strategies. This paper provides an overview of lessons learned on different programs and how that information can be used reduce the cost, schedule, and success of future missions.

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Paul A. Lightsey "Requirements management lessons learned: fuzzy "most likely" versus clean shaven "not to exceed"", Proc. SPIE 9583, An Optical Believe It or Not: Key Lessons Learned IV, 95830B (9 September 2015); https://doi.org/10.1117/12.2196564

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KEYWORDS

Error analysis

Optical alignment

Thermal modeling

Distortion

Fuzzy logic

Monte Carlo methods

Statistical analysis

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