状态受限下四旋翼无人机预定性能控制  

Prescribed Performance Control of Quadrotor with State Constraints

作　　者：高傲 黄宜庆 GAO Ao;HUANG Yiqing(School of Electrical Engineering,Anhui Polytechnic University,Wuhu 241000,China;Anhui Key Laboratory of Electric Drive and Control,Wuhu 241000,China;Key Laboratory of Advanced Perception and Intellgent Control of High-end Equipment,Ministry of Education,Wuhu 241000,China)

机构地区：[1]安徽工程大学电气工程学院,安徽芜湖241000 [2]安徽省电气传动与控制重点实验室,安徽芜湖241000 [3]安徽省高端装备感知与智能控制教育部重点实验室,安徽芜湖241000

出　　处：《安徽工程大学学报》2023年第1期33-41,共9页Journal of Anhui Polytechnic University

基　　金：安徽省自然科学基金资助项目(2108085MF220);安徽高校协同创新基金资助项目(GXXT-2020-069)。

摘　　要：针对四旋翼无人飞行器在姿态控制中存在外界干扰、输入饱和和模型不确定性以及执行器故障问题,提出了一种将反步控制方法与Fuzzy-RBFNN相结合的控制策略。利用反步控制方法具有化整为零的思想,降低无人机系统在控制器设计过程中的难度;利用模糊径向基神经网络的逼近特性,消除模型不确定性以及模拟风场对系统造成的影响;考虑实际系统中执行器的工作效率,利用双曲正切函数代替饱和函数,消除输入饱和对系统造的影响;针对执行器故障问题,设计自适应律在线估计故障因子并且设计Lyapunov函数,从理论上证明了自适应反步Fuzzy-RBFNN控制器设计的稳定性,仿真验证了该控制策略的可行性。A control strategy combining the backstepping control method and Fuzzy-RBFNN is proposed to address the problems of external interference,input saturation,model uncertainty and actuator failure in attitude control of quadrotor unmanned aerial vehicles.The backstepping control method is used to reduce the difficulty of the UAV in the controller design process by adopting the idea of reducing the whole to zero;the approximation characteristics of the fuzzy radial basis neural network are used to eliminate the model uncertainty and the influence of the simulated wind field on the system;the efficiency of the actuator in the actual system is considered and the hyperbolic tangent function is used to simulate the saturation function to eliminate the influence of the input saturation on the system.For the actuator failure problem,the adaptive law is designed,to realize online estimation of fault factors and the Lyapunov function is designed to theoretically prove the stability of the Fuzzy-RBFNN controller design,and the simulation verifies the feasibility of the control strategy.

关 键 词：四旋翼无人飞行器 状态受限 输入饱和 外界干扰 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]