Adaptive neural network based sliding mode altitude control for a quadrotor UAV

Abstract

Reasons and realities such as being non-linear of dynamical equations, being lightweight and unstable nature of quadrotor, along with internal and external disturbances and parametric uncertainties, have caused that the controller design for these quadrotors is considered the challenging issue of the day. In this work, an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor. The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output. Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value. The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it. The superiority of the proposed control method has been proven by comparison and simulation results.

Keywords

adaptive sliding mode controller / analog neural network (ANN) / altitude control of quadrotor / parametric uncertainty

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