Identification of aerodynamic coefficients using computational neural networksPrecise, smooth aerodynamic models are required for implementing adaptive, nonlinear control strategies. Accurate representations of aerodynamic coefficients can be generated for the complete flight envelope by combining computational neural network models with an Estimation-Before-Modeling paradigm for on-line training information. A novel method of incorporating first-partial-derivative information is employed to estimate the weights in individual feedforward neural networks for each aerodynamic coefficient. The method is demonstrated by generating a model of the normal force coefficient of a twin-jet transport aircraft from simulated flight data, and promising results are obtained.
Document ID
19920041159
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Linse, Dennis J. (NASA Langley Research Center Hampton, VA, United States)
Stengel, Robert F. (Princeton University NJ, United States)