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Highly Adaptive Primary Mirror Having Embedded Actuators, Sensors, and Neural Control

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNG05CA17C
Agency Tracking Number: 035108
Amount: $594,993.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 2005
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
115 Jackson Road
Devens, MA 01434
United States
DUNS: 824840649
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Michael Sheedy
 Principal Investigator
 (978) 772-0352
 msheedy@xinetics.com
Business Contact
 Michael Sheedy
Title: Business Official
Phone: (978) 772-0352
Email: msheedy@xinetics.com
Research Institution
N/A
Abstract

Xinetics has demonstrated the technology required to fabricate a self-compensating highly adaptive silicon carbide primary mirror system having embedded actuators, sensors, and neural control with an areal density less that 10Kg/m2. The system architecture complete with feedback sensors, and neural algorithm was conceived, modeled and tested, and appears scaleable to 10-30meter class deployable systems. Highly adaptive telescopes require self-compensating telescope components to enable autonomously optimized optical trains to achieve very low total system wavefront error. High sensitivity semiconductor strain gages were shown to have adequate resolution for shape control. Resistance RTD sensors were shown to provide more than adequate temperature sensitivity. Analysis of strain gage placement conducted during this Phase I showed that the strain sensors required for neural control will require very high precision strain measurement (less than 1 microstrain), potential sensors were tested and characterized. Phase I data acquisition system limitations prevented full closed loop hardware demonstration. As a result Xinetics demonstrated the closed loop function using FEA analysis to provide simulated data to train the MATLAB based neural control algorithm. Phase I results show very encouraging performance and provide design information for a solid technical plan for full hardware demonstration in a phase II.

* Information listed above is at the time of submission. *

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