Nonlinear Flight Control System with Neural Networks
Abstract
In this study an adaptive critic based neural network controller is developed to obtain near optimal control1 laws for a nonlinear automatic flight control system. The adaptive critic approach consists of two neural networks. The first network, called the critic, captures the mapping between the states of a dynamical system and the co-states that arise in an optimal control problem. The second network, called the action network, maps the states of a system to the control. This study uses nonlinear aircraft models in the stall regions from a paper (Garrad and Jordan2 to develop optimal neural controllers for an aircraft; we then compare the results with singular perturbation based nonlinear controllers developed in the literature. The results show that with the neural controllers the aircraft can operate in a broader region of angles of attack beyond stall as compared to other linear and nonlinear controllers. © 2001 by Balakrishnan. Published by The American Institute of Aeronautics and Astronautics Inc.
Recommended Citation
S. Esteban and S. N. Balakrishnan, "Nonlinear Flight Control System with Neural Networks," AIAA Atmospheric Flight Mechanics Conference and Exhibit, American Institute of Aeronautics and Astronautics, Dec 2001.
Department(s)
Mechanical and Aerospace Engineering
International Standard Book Number (ISBN)
978-156347945-8
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Institute of Aeronautics and Astronautics, All rights reserved.
Publication Date
01 Dec 2001
