SBIR-STTR Award

Distributed Adaptive Control of Engine Systems
Award last edited on: 4/9/2010

Sponsored Program
STTR
Awarding Agency
DOD : AF
Total Award Amount
$849,911
Award Phase
2
Solicitation Topic Code
AF08-T026
Principal Investigator
James D Paduano

Company Information

Aurora Flight Sciences Corporation (AKA: 21Tech Corporation)

9950 Wakeman Drive
Manassas, VA 20110
   (703) 369-3633
   pwoodside@aurora.aero
   www.aurora.aero

Research Institution

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Phase I

Contract Number: ----------
Start Date: ----    Completed: ----
Phase I year
2008
Phase I Amount
$99,997
Aurora and Georgia Tech are bringing together a team of experienced control theory, engine design, engine control experts to perform research on next-generation distributed control architectures for turbine engines. The program combines new results in adaptive and distributed control of heterogeneous systems, unsteady modeling of engines, and advanced component control concepts, with the goal of creating system architectures and tools to enable distributed control to be practical and valuable in engines.

Keywords:
Distributed Control, Adaptive Control, Turbine Engine Control, Fadec, Engine Diagnostics, Engine Modeling And Simulation, Smart Engines

Phase II

Contract Number: ----------
Start Date: ----    Completed: ----
Phase II year
2010
Phase II Amount
$749,914
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.

Keywords:
Distributed Control, Adaptive Control, Turbine Engine Control, Fadec, Engine Diagnostics, Engine Mod