Distributed Adaptive Control of Engine Systems

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$749,914.00
Award Year:
2010
Program:
STTR
Phase:
Phase II
Contract:
FA9550-10-C-0039
Award Id:
90163
Agency Tracking Number:
F08A-026-0162
Solicitation Year:
n/a
Solicitation Topic Code:
AF 08T026
Solicitation Number:
n/a
Small Business Information
9950 Wakeman Drive, Manassas, VA, 20110
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
604717165
Principal Investigator:
James Paduano
Senior Autonomy Controls & Est Eng
(617) 500-4807
jpaduano@aurora.aero
Business Contact:
Diana Eichfeld
Contracts Manager
(703) 396-6329
deichfeld@aurora.aero
Research Institute:
Georgia Institute of Technology
R. Paul Hart, Esq.
Office of Sponsored Programs
505 Tenth St. NW
Atlanta, GA, 30332
(404) 894-6929
Nonprofit college or university
Abstract
Aurora and Georgia Tech's Phase I efforts demonstrated the feasibility of a partially distributed control scheme with separate controllers on the engine core and fan, where the controllers are linked by a supervisory controller. This scheme is representative of the situation encountered in VTOL UAV design and the design of new turbo-props and variable pitch turbofans by the large commercial gas turbine manufacturers. In Phase II Aurora proposes to develop the partially distributed controller from Phase I further to cover safe performance during non standard operations (including sensor failure etc.), culminating in a static engine test of a small turbo-prop engine running the developed distributed adaptive controller. BENEFIT: Moving to a distributed architecture will increase flexibility through common standards, improve redundancy properties by improving the overall system topology, and allow for self-diagnosing components and other benefits of 'smart' actuators and sensors, such as reduced harness weight. Distributed computing in the smart components allows for localization of A/D conversion and signal processing, supports open standards and modularity, and provides an opportunity for self-diagnosis. Beyond this, our approach will tap into the full potential of a distributed architecture by allowing the control algorithms themselves to be distributed. This reduces the system's dependence on the FADEC, reducing the number of redundant components and interconnections required to insure reliability. Furthermore, the benefits of adaptation, robustness, and self-repair at the component level are envisioned through feedback control at the component level, improving overall system reliability and performance.

* information listed above is at the time of submission.

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