Abstract
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.
Highlights
In recent years, new and valuable applications of unmanned aerial vehicles (UAV) have emerged in different sectors such as defense, security, construction, agriculture, entertainment, and shipping [1,2,3]
The complexity comes from the randomness of the airstreams and of the exogenous forces, the high nonlinearity dynamics, the coupling between the internal variables, the uncertainty of the measurements, etc
In this work, we propose the design of an intelligent control strategy based on neural networks to cope with these external disturbances, payload changes and wind, that can affect quadrotor dynamics
Summary
New and valuable applications of unmanned aerial vehicles (UAV) have emerged in different sectors such as defense, security, construction, agriculture, entertainment, and shipping [1,2,3]. The complexity comes from the randomness of the airstreams and of the exogenous forces, the high nonlinearity dynamics, the coupling between the internal variables, the uncertainty of the measurements, etc These factors make the techniques based on artificial intelligence a promising approach for the identification and control of these systems [12]. There are some papers where neural networks are applied to model quadrotors [28, 29], and to control them [30,31,32,33], these techniques have not been explored to solve this specific research problem. In this work, we propose the design of an intelligent control strategy based on neural networks to cope with these external disturbances, payload changes and wind, that can affect quadrotor dynamics.
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