This Phase I SBIR project will establish the feasibility of a new class of super-enhanced edge sensors for segmented mirror telescopes. These sensors may be used to deploiy, align, and phase match the primary mirror segments of space based instruments such as NGST. They will be suitable for operational environments ranging from moderately hot (=373°K) to cryogenic (well below 30 °K). Many innovations will be implemented in this new technology. For example, fuzzy logic will be used to provide health and status monitoring and equip each sensor with a self-reporting capability. Artificial neural networks will be employed to provide self-correcting and self-tuning capability. In addition, new error compensation methods will be devised for super accuracy, and multi-mode measurements of both phasing errors and gap separation between neighboring segments. This research is considered critical to both NGST and future NASA missions requiring large segmented primary mirrors. Phase I will entail both experimental testing and computer simulation and modeling. In Phase II the results of Phase I will be reduced to practice and at least two standard model edge sensors will be developed, fully characterized, and documented.
Potential Commercial Applications:This technology will find immediate commercial applications in emerging designs for very large aperture astronomical telescopes for terrestrial observatories. Major aerospace contractors also present significant market opportunities for commercially produced edge sensors. The results of this research are also directly applicable to a broad range of sensors and actuators other than edge sensors. It is expected that the same hardware and software may be applied to industrial sensors and controls for greatly enhanced performance and reliability in factory automation.
