Neuro-optimal Control of an Unmanned Helicopter

Abstract

Helicopter unmanned aerial vehicles (UAVs) can be extensively used for military missions as well as in civil operations, ranging from multi-role 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 tracking of an underactuated helicopter using an adaptive critic neural network (NN) 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 without using value and policy iterations. In the proposed technique, optimal tracking is accomplished by a single NN, which is tuned online using a novel weight update law. Stability analysis is performed and simulation results demonstrate the proposed control design. © 2012 The Society for Modeling.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

Army Research Laboratory, Grant W911NF-10-2-0077

Keywords and Phrases

Hamilton-Jacobi-Bellman (HJB) equation; Helicopter unmanned aerial vehicle (UAV); Neural network (NN); Nonlinear optimal control; Online approximator (OLA); Trajectory tracking

International Standard Serial Number (ISSN)

1557-380X; 1548-5129

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 SAGE Publications, All rights reserved.

Publication Date

01 Jan 2014

Link to Full Text

Share

 
COinS