摘要
本文针对四旋翼无人机研究了鲁棒反步姿态控制策略.由于四旋翼无人机结构复杂,其非线性数学模型难以精确建立,因此在控制器设计过程中需要综合考虑模型不确定性、未知外部干扰、输入饱和以及姿态受限等因素.针对模型中的不确定项,使用神经网络进行逼近;对于外部未知干扰,使用非线性干扰观测器进行补偿;使用双曲正切函数逼近饱和函数,解决输入饱和问题;同时使用界限Lyapunov函数设计控制器,确保姿态满足限制条件.最后,设计四旋翼无人机反步姿态控制器,并根据Lyapunov稳定性定理证明了闭环控制系统的有界稳定.仿真结果表明了所研究控制方法的有效性.
We investigate the robust backstepping-based attitude control strategy for a quadrotor unmanned aircraft vehicle(UAV). Because of its complex structure, a nonlinear mathematical model of UAV is difficult to be built accurately.Thus, in the process of controller design, we need to consider the following factors comprehensively: model uncertainties,unknown external disturbances, input saturations and attitude constraints. A neural network(NN) method is employed to estimate the model uncertainties of the system; the nonlinear disturbance observer is introduced to compensate for the external disturbances; the hyperbolic tangent function is used to approximate the saturation function for solving the input saturation problem. In designing the controller, we use the barrier Lyapunov function(BLF) to guarantee the constraints of the attitude. Finally, a backstepping-based controller is developed to control the attitude of the quadrotor UAV. The closed-loop control system is proved to be uniformly bounded by using Lyapunov stability theory. Simulation results are given to demonstrate the effectiveness of the proposed control strategy.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2015年第10期1361-1369,共9页
Control Theory & Applications
基金
国家自然科学基金项目(61573184
61374212)
江苏省自然科学基金项目(SBK20130033)
教育部博士点基金(20133218110013)
江苏省六大高峰人才项目(2012-XXRJ-010)
南京航空航天大学研究生创新基地开放基金项目(kfjj20130206)资助~~
关键词
四旋翼无人机
反步
非线性干扰观测器
神经网络
受限控制
quadrotor UAV
backstepping
nonlinear disturbance observer
neural networks
limited control