Fuzzy Adaptive Neurons Applied to the Identification of Parameters and Trajectory Tracking Control of a Multi-Rotor Unmanned Aerial Vehicle Based on Experimental Aerodynamic Data

2020 ◽  
Vol 100 (2) ◽  
pp. 647-665
Author(s):  
A. M. E. Ramírez-Mendoza ◽  
J. R. Covarrubias-Fabela ◽  
L. A. Amezquita-Brooks ◽  
O. García-Salazar ◽  
W. Yu
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Trajectory Tracking Control ◽  
Identification Of Parameters ◽  
Aerial Vehicle ◽  
Fuzzy Adaptive

Finite-time trajectory tracking control for a 12-rotor unmanned aerial vehicle with input saturation

2018 ◽  
Vol 81 ◽  
pp. 52-62 ◽  
Author(s):  
Chunyang Fu ◽  
Yantao Tian ◽  
Haiyang Huang ◽  
Lei Zhang ◽  
Cheng Peng
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Trajectory Tracking ◽  
Finite Time ◽  
Tracking Control ◽  
Input Saturation ◽  
Trajectory Tracking Control ◽  
Aerial Vehicle

scholarly journals Modeling and Robust Trajectory Tracking Control for a Novel Six-Rotor Unmanned Aerial Vehicle

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Chengshun Yang ◽  
Zhong Yang ◽  
Xiaoning Huang ◽  
Shaobin Li ◽  
Qiang Zhang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Controller Design ◽  
Sliding Mode ◽  
Trajectory Tracking Control ◽  
Sliding Mode Disturbance Observer ◽  
Aerial Vehicle ◽  
Differential Expansion ◽  
Derivatives Of

Modeling and trajectory tracking control of a novel six-rotor unmanned aerial vehicle (UAV) is concerned to solve problems such as smaller payload capacity and lack of both hardware redundancy and anticrosswind capability for quad-rotor. The mathematical modeling for the six-rotor UAV is developed on the basis of the Newton-Euler formalism, and a second-order sliding-mode disturbance observer (SOSMDO) is proposed to reconstruct the disturbances of the rotational dynamics. In consideration of the under-actuated and strong coupling properties of the six-rotor UAV, a nested double loops trajectory tracking control strategy is adopted. In the outer loop, a position error PID controller is designed, of which the task is to compare the desired trajectory with real position of the six-rotor UAV and export the desired attitude angles to the inner loop. In the inner loop, a rapid-convergent nonlinear differentiator (RCND) is proposed to calculate the derivatives of the virtual control signal, instead of using the analytical differentiation, to avoid “differential expansion” in the procedure of the attitude controller design. Finally, the validity and effectiveness of the proposed technique are demonstrated by the simulation results.

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