Abstract
A neural network (NN) based output feedback controller for a quadrotor unmanned aerial vehicle (UAV) is proposed. The NNs are utilized in the observer and for generating virtual and actual control inputs, respectively, where the NNs learn the nonlinear dynamics of the UAV online including uncertain nonlinear terms like aerodynamic friction and blade flapping. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semi-globally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle.
Recommended Citation
J. Sarangapani and T. A. Dierks, "Neural Network Output Feedback Control of a Quadrotor UAV," Proceedings of the 47th IEEE Conference on Decision and Control, 2008, CDC 2008, Institute of Electrical and Electronics Engineers (IEEE), Dec 2008.
The definitive version is available at https://doi.org/10.1109/CDC.2008.4738814
Meeting Name
47th IEEE Conference on Decision and Control, 2008, CDC 2008
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Sponsor(s)
National Science Foundation (U.S.)
United States. Department of Education
Keywords and Phrases
Lyapunov Method; Neural Network; Observer; Output Feedback; Quadrotor UAV
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Dec 2008
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
