Neuro-optimal Control of Helicopter UAVs

Abstract

Helicopter UAVs can be extensively used for military missions as well as in civil operations, ranging from multirole combat support and search and rescue, to border surveillance and forest fire monitoring. Helicopter UAVs are underactuated nonlinear mechanical systems with correspondingly challenging controller designs. This paper presents an optimal controller design for the regulation and vertical tracking of an underactuated helicopter using an adaptive critic neural network framework. the online approximator-Based controller learns the infinite-horizon continuous-time Hamilton-Jacobi-Bellman (HJB) equation and then calculates the corresponding optimal control input that minimizes the HJB equation forward-in-time. in the proposed technique, optimal regulation and vertical tracking is accomplished by a single neural network (NN) with a second NN necessary for the virtual controller. Both of the NNs are tuned online using novel weight update laws. Simulation results are included to demonstrate the effectiveness of the proposed control design in hovering applications. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

helicopter UAV; HJB equation; hovering; neural network (NN); Nonlinear optimal control; online approximator (OLA)

International Standard Book Number (ISBN)

978-081948619-6

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

26 Sep 2011

Link to Full Text

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