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Model-Based Engine Control Architecture with an Extended Kalman FilterThis paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.
Document ID
20160002248
Acquisition Source
Glenn Research Center
Document Type
Technical Memorandum (TM)
Authors
Csank, Jeffrey T.
(NASA Glenn Research Center Cleveland, OH United States)
Connolly, Joseph W.
(NASA Glenn Research Center Cleveland, OH United States)
Date Acquired
February 24, 2016
Publication Date
February 1, 2016
Subject Category
Aircraft Propulsion And Power
Report/Patent Number
GRC-E-DAA-TN29222
NASA/TM-2016-219066
E-19199
Meeting Information
Meeting: AIAA SciTech 2016
Location: San Diego, CA
Country: United States
Start Date: January 4, 2016
End Date: January 8, 2016
Sponsors: American Inst. of Aeronautics and Astronautics
Funding Number(s)
WBS: WBS 109492.02.03.02.11
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Keywords
engine control
control systems design
control theory
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