The problem of position tracking of a mini drone subject to wind perturbations is investigated. The solution is based on a detailed unmanned aerial vehicle (UAV) model, with aerodynamic coefficients and external disturbance components, which is introduced in order to better represent the impact of the wind field. Then, upper bounds of wind-induced disturbances are characterized, which allow a sliding mode control (SMC) technique to be applied with guaranteed convergence properties. The peculiarity of the considered case is that the disturbance upper bounds depend on the control amplitude itself (i.e. the system is nonlinear in control), which leads to a new procedure for the control tuning presented in the paper. The last part of the paper is dedicated to the analysis and reduction of chattering effects, as well as investigation of rotor dynamics issues. Conventional SMC with constant gains, proposed first order SMC, and proposed quasi-continuous SMC are compared. Nonlinear UAV simulator, validated through indoor experiments, is used to demonstrate the effectiveness of the proposed controls.

This paper investigates the use of a data-driven method to model the dynamics of the chaotic Lorenz system. An architecture based on a recurrent neural network with long and short term dependencies predicts multiple time steps ahead the position and velocity of a particle using a sequence of past states as input. To account for modeling errors and make a continuous forecast, a dense artificial neural network assimilates online data to detect and update wrong predictions such as non-relevant switchings between lobes. The data-driven strategy leads to good prediction scores and does not require statistics of errors to be known, thus providing significant benefits compared to a simple Kalman filter update.