Neural-network-Based Navigation and Control of Unmanned Aerial Vehicles for Detecting Unintended Emissions

Abstract

Unmanned Aerial Vehicles (UAVs) are versatile aircraft with many applications, including the potential for use to detect unintended electromagnetic emissions from electronic devices. a particular area of recent interest has been helicopter unmanned aerial vehicles. Because of the nature of these helicopters' dynamics, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via output feedback control for trajectory tracking of a helicopter UAV using a neural network (NN). the output-feedback control system utilizes the backstepping methodology, employing kinematic, virtual, and dynamic controllers and an observer. Optimal tracking is accomplished with a single NN utilized for cost function approximation. the controller positions the helicopter, which is equipped with an antenna, such that the antenna can detect unintended emissions. the overall closed-loop system stability with the proposed controller is demonstrated by using Lyapunov analysis. Finally, results are provided to demonstrate the effectiveness of the proposed control design for positioning the helicopter for unintended emissions detection. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

Optimal Control; Unintended Emission Detection; Unmanned Helicopter UAV

International Standard Book Number (ISBN)

978-081949065-0

International Standard Serial Number (ISSN)

0277-786X

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.

Publication Date

01 Dec 2012

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