针对四旋翼无人飞行器质量未知情况下的垂直起降控制问题,提出一种基于状态反馈和神经网络自适应的混合控制方法.该方法通过一个状态反馈控制器实现飞行器的水平位置和航向控制,考虑到飞行器负载的未知特性,通过径向基函数(RBF)神经网络对飞行器质量进行估计,从而实现对高度的精确控制.仿真分析及验证表明,所提出的控制方法能够有效实现飞行器高度的精确控制,并能够在线估计出飞行器质量参数.%A hybrid control method based on state feedback and adaptive neural network was proposed, which considered the taking off and landing control problem under unknown mass of the Unmanned Aerial Vehicle (UAV). A state feedback controller was designed to realize the horizontal position and heading control. The accurate control of height was archived considering the vehicle's unknown load through the Radial Basis Function (RBF) neural network. The simulation analysis and experiments illustrate that the proposed control method can effectively realize the accurate control of height, and can be able to online estimate aircraft quality parameters.
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