Concurrent Learning Enabled Adaptive Limit Detection for Active Pilot Cueing

Date

2014-09-01

Author

Gursoy, Gonenc
Yavrucuk, İlkay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Neural-network-based adaptive dynamic models are commonly used to estimate allowable control travel and the proximity to a limiting flight condition in the design of advanced envelope protection algorithms for fly-by-wire aircraft. In this paper, linear models are compensated with adaptive neural networks, which use instantaneous sensor data as well as past flight history information for concurrent learning. A law for collecting appropriate training data into the history stack is established. It is observed that using the proposed time history data for online neural network training provides more accurate dynamic trim and control limit predictions compared to using instantaneous sensor data only. Simulation results for a fixed-wing aircraft during maneuvers show comparisons between the different adaptation schemes.

Subject Keywords

Smart-gain concepts

URI

https://hdl.handle.net/11511/38960

Journal

JOURNAL OF AEROSPACE INFORMATION SYSTEMS

DOI

https://doi.org/10.2514/1.i010205

Collections

Department of Aerospace Engineering, Article

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Citation Formats

G. Gursoy and İ. Yavrucuk, “Concurrent Learning Enabled Adaptive Limit Detection for Active Pilot Cueing,” JOURNAL OF AEROSPACE INFORMATION SYSTEMS, pp. 542–550, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38960.