基于神经网络的四旋翼无人机自适应非奇异快速终端滑模容错控制
Adaptive Non-Singular Fast Terminal Sliding Mode Fault-Tolerant Control of Quadrotor UAV Based on Neural Network
摘要: 针对具有外部干扰、系统模型不确定性和执行器故障的四旋翼无人机系统，提出了一种基于RBF神经网络的自适应非奇异快速终端滑模控制算法的有限时间容错控制方案。首先将无人机动力学模型转换为位置子系统和姿态子系统，使用全局快速终端滑模控制方法实现位置子系统容错控制，在姿态子系统中，设计了一种基于RBF神经网络的自适应非奇异快速终端滑模容错控制器，并采用Lyapunov稳定性理论证明了控制器的稳定性。最后通过仿真对比实验验证了所提控制方案的有效性和优越性。
Abstract: A finite-time fault-tolerant control scheme based on an adaptive non-singular fast terminal sliding mode control algorithm with RBF neural network is proposed for a quadrotor UAV system with external disturbances, system model uncertainties and actuator faults. Firstly, the UAV dynamics model is converted into a position subsystem and an attitude subsystem. The global fast terminal sliding mode control method is used to achieve the fault-tolerant control of the position subsystem. An adaptive non-singular fast terminal sliding mode fault-tolerant controller based on the RBF neural network is designed in the attitude subsystem. The stability of the controller is proved using the Lyapunov stability theory. Finally, simulation and comparison experiments verify the proposed control scheme’s effectiveness and superiority.
文章引用：陈伟东, 李家伟, 梁传福. 基于神经网络的四旋翼无人机自适应非奇异快速终端滑模容错控制[J]. 传感器技术与应用, 2024, 12(6): 839-852. https://doi.org/10.12677/jsta.2024.126092

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