Abstract
Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking. © 2012 IEEE.
Recommended Citation
D. Nodland et al., "Neural Network-Based Optimal Adaptive Output Feedback Control of a Helicopter Uav," IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 7, pp. 1061 - 1073, article no. 6487408, Institute of Electrical and Electronics Engineers, Apr 2013.
The definitive version is available at https://doi.org/10.1109/TNNLS.2013.2251747
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Hamilton-Jacobi-Bellman (HJB) equation; helicopter unmanned aerial vehicle (UAV); neural network (NN); nonlinear optimal control
International Standard Serial Number (ISSN)
2162-2388; 2162-237X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Apr 2013
