Model-Based Engine Control Architecture with an Extended Kalman Filter

Csank, Jeffrey T.; Connolly, Joseph W.

This 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)

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Date Acquired

February 24, 2016

Publication Date

February 1, 2016

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Report/Patent Number

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

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Public

Work of the US Gov. Public Use Permitted.

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