Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
IEEE Transactions on Fuzzy Systems
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
IEEE Transactions on Neural Networks
A Harmonic Potential Approach for Simultaneous Planning and Control of a Generic UAV Platform
Journal of Intelligent and Robotic Systems
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems
Journal of Field Robotics
Modeling and Adaptive Tracking Control of a Quadrotor UAV
International Journal of Intelligent Mechatronics and Robotics
Hard Real-Time Implementation of a Nonlinear Controller for the Quadrotor Helicopter
Journal of Intelligent and Robotic Systems
Journal of Intelligent and Robotic Systems
Adaptive Nonlinear Stabilization Control for a Quadrotor UAV: Theory, Simulation and Experimentation
Journal of Intelligent and Robotic Systems
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In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. 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 semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.