基于RBF与BP神经网络的四旋翼编队滑模控制

Sliding Mode Control of Quadrotor Formation Based on RBF and BP Neural Network

针对四旋翼编队队形保持控制中常用简化自驾仪来代替内回路并未能贴合现实模型的问题,以及常受扰动和控制器调参数困难从而引起控制精度下降的现象,提出一种基于RBF与BP神经网络的滑模编队控制器。首先采用Leader-Follower思想对四旋翼编队问题进行建模,并基于RBF神经网络逼近不确定项与干扰项来设计内回路轨迹跟踪控制器。在此基础上根据机间通信关系设计编队保持控制器并结合BP神经网络对控制器参数进行整定,最后利用Lyapunov方法证明其稳定性。仿真结果表明,所提控制器具有良好的抗干扰能力,并且能有效提高队形保持精度,使编队保持稳定期望队形。

In order to solve the problems that the simplified autopilot is often used to replace the inner loop in the formation keeping control of quadrotor formation which can not fit the real model,and the decline of control accuracy due to disturbance and difficult controller parameter ajustment,a sliding mode formation controller based on RBF and BP neural network is proposed.Firstly,the Leader-Follower concept is used to model the formation problem of quadrotor,and the inner-loop trajectory tracking controller is designed based on RBF neural network approaching uncertainties and interference terms.On this basis,the formation keeping controller is designed according to the communication relationship between UAVs,and the parameters of the controller are adjusted by BP neural network.Finally,the stability is proved by Lyapunov method.The simulation results show that the proposed controller has good anti-interference ability,and can effectively improve the accuracy of formation keeping and keep the a stable and expected formation.