基于RBF网络的四旋翼无人机姿态鲁棒自适应反步滑模控制  

Robust adaptive backstepping sliding mode attitude control of quadrotor UAV based on RBF network

作　　者：刘金华[1] 王远 张智轩 李涛[1,2] LIU Jinhua;WANG Yuan;ZHANG Zhixuan;LI Tao(School of Automation,Nanjing University of Information Science and Technology,Nanjing,Jiangsu 210044,China;Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing University of Information Science and Technology,Nanjing,Jiangsu 210044,China)

机构地区：[1]南京信息工程大学自动化学院,江苏南京210044 [2]南京信息工程大学大气环境与装备技术协同创新中心,江苏南京210044

出　　处：《江苏大学学报（自然科学版）》2025年第1期36-42,共7页Journal of Jiangsu University：Natural Science Edition

基　　金：江苏省“333工程”计划项目(BRA2020067)。

摘　　要：针对存在干扰的四旋翼无人机姿态系统,设计了一种RBF网络鲁棒自适应反步滑模控制器.在反步滑模控制的基础上,通过RBF网络逼近和补偿标称控制律,采用神经网络最小参数学习法,取神经网络的权值上界估计作为神经网络的估计值,通过设计参数估计自适应律来代替神经网络权值的调整,并用Lyapunov理论证明系统的稳定性.仿真结果表明:该方法相比反步滑模控制方法,在有干扰的情况下,有更短的调节时间,更好的跟踪精度,验证了本方法具有更好的抗干扰性和鲁棒性.The robust backstepping sliding mode RBF network adaptive controller was proposed for the quadrotor unmanned aerial vehicle(UAV)attitude system with disturbance.Based on the backstepping sliding mode control,the RBF network was used to approximate and compensate the ideal control law.The minimum parameter learning method of the neural network was adopted,and the weight upper bound of the neural network was estimated as estimated value of the neural network.The adaptation law was used to replace the adjustment of neural network weights,and Lyapunov theorem was used to prove the stability of system.The simulation results show that compared with the backstepping sliding mode control method,the proposed method has shorter adjustment time and better tracking accuracy in the case of disturbance.It is verified that the proposed method has better anti-interference and robustness.

关 键 词：四旋翼无人机 姿态控制 反步滑模控制 RBF神经网络 鲁棒自适应控制 

分 类 号：V249.1[航空宇航科学与技术—飞行器设计]