输入饱和与姿态受限的四旋翼无人机反步姿态控制

Backstepping-based attitude control for a quadrotor UAV with input saturation and attitude constraints

中文关键词  四旋翼无人机  反步  非线性干扰观测器  神经网络  受限控制

英文关键词  quadrotor UAV  backstepping  nonlinear disturbance observer  neural networks  limited control

中文摘要

      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.

英文摘要

      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.