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.

Meeting Name

47th IEEE Conference on Decision and Control, 2008, CDC 2008


Electrical and Computer Engineering

Second Department

Computer Science


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





© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.