基于自适应RBF神经网络的无人驾驶飞行汽车高度控制方法研究

With the growing population and crowded urban roads, the concept of flying cars will solve this problem very well. Therefore, flying cars will be an important direction of scientific and technological development in the future. For a flying car that needs to carry people, the stability of its controller is the first to bear the brunt. Based on this, this paper attempts to use the adaptive method of RBF neural network combined with sliding mode control to control its altitude channel. Based on the parameters of four-rotor UAV, the good control effect of RBF neural network is verified by simulation experiments, which lays a theoretical foundation for the subsequent application of this control method to the control of flying vehicles. It is concluded that RBF neural network can predict and eliminate the interference during flight, and the change of time constant will not affect the control of aircraft, which lays a good foundation for subsequent attitude control of flying vehicles, and provides a new idea for promoting the development of flying vehicles.