Concurrent Learning Enabled Adaptive Limit Detection for Active Pilot Cueing

Date

2010-08-02

Author

Yavrucuk, İlkay

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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 to build adaptive models of relevant aircraft dynamics. Neural networks are trained online not only using instantaneous sensor data, but also a stack of data collected during flight. The additional stack is made up of transient data as well as a collection of data representing steady state conditions. It is observed that using time history data stacks for online neural network training provides more accurate dynamic trim and control limit predictions compared to using instantaneous data only. Simulation results for a fixed wing aircraft during maneuvers, show comparisons between the different adaptation schemes.

URI

https://hdl.handle.net/11511/73834

DOI

https://doi.org/10.2514/6.2010-7945

Conference Name

AIAA Guidance, Navigation and Control Conference

Collections

Department of Aerospace Engineering, Conference / Seminar

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Online Dynamic Trim and Control Limit Estimation Yavrucuk, İlkay (American Institute of Aeronautics and Astronautics (AIAA), 2012-9) The online estimation of a maneuvering steady-state condition of an aircraft, called the dynamic trim, is used to estimate the allowable control travel during flight, a key information in pilot cueing for envelope limit protection. In this paper a new methodology is presented where adaptive models are used to estimate online local dynamic trim conditions, while requiring very limited a priori vehicle information. Adaptive neural networks are employed to enable online learning. The models are used to estimat...
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Citation Formats

İ. Yavrucuk, “Concurrent Learning Enabled Adaptive Limit Detection for Active Pilot Cueing,” presented at the AIAA Guidance, Navigation and Control Conference, 2010, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73834.