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  • 1
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    Neural networks for aircraft system identification (1991)
    In:  CASI
    Details
    Publication Date: 2013-08-31
    Description: Artificial neural networks offer some interesting possibilities for use in control. Our current research is on the use of neural networks on an aircraft model. The model can then be used in a nonlinear control scheme. The effectiveness of network training is demonstrated.
    Keywords: CYBERNETICS
    Type: NASA. Langley Research Center, Joint University Program for Air Transportation Research, 1990-1991; p 141-154
    Format: application/pdf
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  • 2
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    Neural networks for function approximation in nonlinear control (1990)
    In:  Other Sources
    Details
    Publication Date: 2019-06-28
    Description: Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.
    Keywords: CYBERNETICS
    Format: text
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  • 3
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    Neural Networks in Nonlinear Aircraft Control (1990)
    In:  CASI
    Details
    Publication Date: 2014-09-23
    Description: Recent research indicates that artificial neural networks offer interesting learning or adaptive capabilities. The current research focuses on the potential for application of neural networks in a nonlinear aircraft control law. The current work has been to determine which networks are suitable for such an application and how they will fit into a nonlinear control law.
    Keywords: AIRCRAFT STABILITY AND CONTROL
    Type: NASA, Langley Research Center, Joint University Program for Air Transportation Research, 1989-1990; p 151-161
    Format: application/pdf
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  • 4
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    System Identification for Nonlinear Control Using Neural Networks (1990)
    In:  CASI
    Details
    Publication Date: 2019-06-28
    Description: An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.
    Keywords: AIRCRAFT STABILITY AND CONTROL
    Type: NASA, Langley Research Center, Joint University Program for Air Transportation Research, 1989-1990; p 187-192
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  • 5
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    Surveillance and Datalink Communication Performance Analysis for Distributed Separation Assurance System Architectures (2012)
    In:  CASI
    Details
    Publication Date: 2019-07-12
    Description: This study investigates the effects of two technical enablers: Automatic Dependent Surveillance - Broadcast (ADS-B) and digital datalink communication, of the Federal Aviation Administration s Next Generation Air Transportation System (NextGen) under two separation assurance (SA) system architectures: ground-based SA and airborne SA, on overall separation assurance performance. Datalink performance such as successful reception probability in both surveillance and communication messages, and surveillance accuracy are examined in various operational conditions. Required SA performance is evaluated as a function of subsystem performance, using availability, continuity, and integrity metrics to establish overall required separation assurance performance, under normal and off-nominal conditions.
    Keywords: Air Transportation and Safety
    Type: NASA/CR-2012-217590 , NF1676L-14554
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  • 6
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    Identification of aerodynamic coefficients using computational neural networks (1993)
    In:  Other Sources
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    Publication Date: 2019-08-28
    Keywords: AERODYNAMICS
    Type: Journal of Guidance, Control, and Dynamics (ISSN 0731-5090); 16; 6; p. 1018-1025.
    Format: text
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  • 7
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    Identification of aerodynamic coefficients using computational neural networks (1992)
    In:  Other Sources
    Details
    Publication Date: 2019-08-27
    Description: Precise, 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.
    Keywords: AERODYNAMICS
    Type: AIAA PAPER 92-0172
    Format: text
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  • 8
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    A system identification model for adaptive nonlinear control (1991)
    In:  Other Sources
    Details
    Publication Date: 2019-08-28
    Description: A system identification model that combines generalized-spline function approximation with a nonlinear control system is described. The complete control system contains three main elements: a nonlinear-inverse-dynamic control law that depends on a comprehensive model of the plant, a state estimator whose outputs drive the control law, and a function approximation scheme that models the system dynamics. The system-identification task, which combines an extended Kalman filter with a function approximator modeled as an artificial neural network, is considered. The results of an application of the identification techniques to a nonlinear transport aircraft model are presented.
    Keywords: CYBERNETICS
    Type: 1991 American Control Conference; Jun 26, 1991 - Jun 28, 1991; Boston, MA; United States
    Format: text
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