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Adaptive observer-based fault detection and active tolerant control for unmanned aerial vehicles attitude system

机译：无人机姿态系统的自适应观察者的故障检测和活性耐受控制

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摘要

A robust adaptive observer combined with radial basis function neural network (RBFNN) is designed for the unmanned aerial vehicles (UAVs) fault-detection system is proposed in this paper. Firstly, the fault dynamics model with unknown disturbance of the unmanned aerial vehicle’s attitude system is established, and a robust adaptive observer combined with radial basis function neural network is designed for the vehicle’s fault-detection, then, the detected fault combined with a robust controller is applied to design the fault-tolerant controller. In the end, the stability and effective of the fault detection and tolerant system is proved by Lyapunov theory and simulation.
机译：与径向基函数神经网络（RBFNN）相结合的鲁棒自适应观察者专为本文提出了无人驾驶飞行器（UAVS）故障检测系统。首先，建立了具有未知无人机态度系统的干扰的故障动力学模型，并设计了一种与径向基函数神经网络相结合的鲁棒自适应观察者为车辆的故障检测设计，检测到的故障与鲁棒控制器组合应用于设计容错控制器。最后，利普吞噬理论和模拟证明了故障检测和耐受系统的稳定性和有效性。

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