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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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Concurrent Learning Enabled Adaptive Limit Detection for Active Pilot Cueing
Gursoy, Gonenc; Yavrucuk, İlkay (2014-09-01)
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...
Long Term Learning Adaptive Neural Network Estimator Based Limit Detection
Yavrucuk, İlkay (null; 2010-08-26)
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 s...
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...
Direct Adaptive Limit and Control Margin Estimation with Concurrent Learning
Gursoy, Gonenc; Yavrucuk, İlkay (American Institute of Aeronautics and Astronautics (AIAA), 2016-6)
In this paper, two vital signals to enable flight envelope protection, namely the onset to the flight envelope (limit margin) and the available control travel to reach the limit boundary (control margin), are estimated using improved noniterative adaptive neural-network-based approximate models. The adaptive elements use current and past information (concurrent learning) and have guaranteed signal bounds. Minimum singular value maximization is used to record data for concurrent learning. Results showed bett...
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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.