Long Term Learning Adaptive Neural Network Estimator Based Limit Detection

Date

2010-08-26

Author

Yavrucuk, İlkay

Metadata

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Dynamic adaptive 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. A stack of data collected during flight is used to update the network weights online. The data stack is made up of instantaneously measured data and recorded data during simulations. It is observed that by using recorded data in a stack can cancel out new modeling errors in a short time and results with better predictions of approaching limits compared to using instantaneous data only

Subject Keywords

Limit avoidance, Limit detection, Pilot cueing

URI

https://hdl.handle.net/11511/83455

DOI

https://doi.org/10.3182/20100826-3-tr-4015.00052

Conference Name

10th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (26 Ağustos - 28 Ekim 2010)

Collections

Department of Aerospace Engineering, Conference / Seminar

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Citation Formats

İ. Yavrucuk, “Long Term Learning Adaptive Neural Network Estimator Based Limit Detection,” presented at the 10th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (26 Ağustos - 28 Ekim 2010), Antalya, Turkey, 2010, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/83455.