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.

摘要

由于动力学方程的非线性、四转子的轻量化和不稳定性等原因和现实情况, 加上内、外扰动和 参数不确定性等因素, 使得四转子控制器的设计成为当今研究的热点。本文提出一种基于神经网络的 自适应滑模控制器来控制四转子的高度。四转子高度的误差和误差导数是神经网络的输入, 高度滑动 面变量是输出。神经网络根据四转子的条件自适应地估计滑动面变量, 并将四转子的高度设为期望值。 用李雅普诺夫理论证明了该控制器的稳定性, 证明了系统的所有状态都达到了滑动面, 并保持在滑动 面内。通过对比和仿真, 验证了所提出的控制方法的优点。