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.
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 http://dx.doi.org/10.1109/CDC.2008.4738814
47th IEEE Conference on Decision and Control, 2008, CDC 2008
Electrical and Computer Engineering
National Science Foundation (U.S.)
United States. Department of Education
Keywords and Phrases
Lyapunov Method; Neural Network; Observer; Output Feedback; Quadrotor UAV
Article - Conference proceedings
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