By Topic

Neural network based adaptive dynamic surface control for flight path angle

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yi Guo ; Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China ; Jinkun Liu

A neural network based adaptive dynamic surface control is proposed for the aircraft longitudinal flight path angle. The dynamic surface control method eliminates the problem of “explosion of complexity” existing in traditional backstepping approach with the introduction of low pass filters. Radial basis function (RBF) neural networks are used to approximate the unknown nonlinearities of the model online. Adaptive laws are designed to estimate the weight values of the neural networks and unknown parameters. From Lyapunov stability analysis, it is shown that the control strategy can guarantee the semi-global practical tracking and arbitrarily small tracking error by adjusting the controller parameters. Simulation results are presented to validate the good tracking performance and strong adaptability of the control system.

Published in:

Decision and Control (CDC), 2012 IEEE 51st Annual Conference on

Date of Conference:

10-13 Dec. 2012