Abstract
Helicopter unmanned aerial vehicles (UAVs) may be widely used for both military and civilian operations. Because these helicopters are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper presents an optimal controller design for trajectory tracking of a helicopter UAV using a neural network (NN). the state-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers. the online approximator-Based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman (HJB) equation in continuous time and calculates the corresponding optimal control input to minimize the HJB equation forward-in-time. Optimal tracking is accomplished with a single NN utilized for cost function approximation. the overall closed-loop system stability is demonstrated using Lyapunov analysis, with the position, orientation, angular and translational velocity tracking errors, and NN weight estimation errors uniformly ultimately bounded (UUB) in the presence of bounded disturbances and NN functional reconstruction errors. © 2011 IEEE.
Recommended Citation
D. Nodland et al., "Neural Network-Based Optimal Control for Trajectory Tracking of a Helicopter UAV," Proceedings of the IEEE Conference on Decision and Control, pp. 3876 - 3881, article no. 6160554, Institute of Electrical and Electronics Engineers, Jan 2011.
The definitive version is available at https://doi.org/10.1109/CDC.2011.6160554
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Book Number (ISBN)
978-161284800-6
International Standard Serial Number (ISSN)
2576-2370; 0743-1546
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2011
