Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors

• Published in: Complexity (New York, N.Y.) Vol. 2019; no. 2019; pp. 1 - 20
• Main Authors: Sierra, Jesús Enrique, Santos, Matilde
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Adaptive control; Computer simulation; Distance learning; International conferences; Machine learning; Neural networks; Parameter estimation; Proportional integral derivative; Robotics; Robust control; Rotary wing aircraft; Unmanned aerial vehicles; Unmanned helicopters; Vehicles
• ISSN: 1076-2787; 1099-0526; 1099-0526
• DOI: 10.1155/2019/6460156

In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles (UAV) dynamics. Specifically, the variation of the system mass during logistic tasks and the influence of the wind are considered. An adaptive neuromass estimator and an adaptive neural disturbance estimator complement the action of a set of PID controllers, stabilizing the UAV and improving the system performance. The control strategy has been extensively tested with different trajectories: linear, helical, circular, and even a lemniscate one. During the experiments, the mass of the UAV is triplicated and winds of 6 and 9 in Beaufort’s scale are introduced. Simulation results show how the online learning of the estimator increases the robustness of the controller, reducing the effects of the changes in the mass and of the wind on the quadrotor.