Robust adaptive control
Controlling a drone: Comparison between a based model method and a fuzzy inference system
Applied Soft Computing
Backstepping Approach for Controlling a Quadrotor Using Lagrange Form Dynamics
Journal of Intelligent and Robotic Systems
Image-based visual servo control of the translation kinematics of a quadrotor aerial vehicle
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
An integral predictive/nonlinear H∞ control structure for a quadrotor helicopter
Automatica (Journal of IFAC)
Output feedback control of a quadrotor UAV using neural networks
IEEE Transactions on Neural Networks
Stabilization and Trajectory Tracking of a Quad-Rotor Using Vision
Journal of Intelligent and Robotic Systems
Vision Based Position Control for MAVs Using One Single Circular Landmark
Journal of Intelligent and Robotic Systems
Journal of Intelligent and Robotic Systems
A Practical Visual Servo Control for an Unmanned Aerial Vehicle
IEEE Transactions on Robotics
Adaptive Position Tracking of VTOL UAVs
IEEE Transactions on Robotics
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The dynamics of UAV's have special features that can complicate the process of designing a trajectory tracking controller. In this paper, after modelling the quadrotor as a VTOL UAV, a nonlinear adaptive controller is designed to solve trajectory tracking problem in the presence of parametric and nonparametric uncertainties. This controller doesn't need knowing any physical parameters of the quadrotor, and there isn't need to retune the controller for various payloads. In this approach, the control of a quadrotor is performed by using decentralized adaptive controllers in the inner attitude control and outer translational movement control loops. The outer loop generates the instantaneous desired angles for inner loop. The inner loop stabilizes the orientation of the vehicle. Inverse kinematic of robot is used to convert outputs of the outer loop to inputs of the inner loop. The controller needs some unknown physical parameter to generate control signals. A robust parameter identifier estimates the required parameters for the outer control loops. Simulations are carried out to illustrate the robustness and tracking performance of the controllers.