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Fault detection and identification in Quadrotor system (Quadrotor robot)

机译:四旋翼系统(四旋翼机器人)中的故障检测和识别

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Fault Detection and Identification (FDI) monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A Quadrotor robot is used to represent a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. This dynamic model is based on the first principles of the Quadrotor: Propeller model and its force as well as moments generation. The Quadrotor controller is designed such that it can be controlled using both the attitude control (inner loop) and position control (outer loop). PD controller used the Phi, Theta, Psi, x, y and z as a reference to adjust the attitude and position of the Quadrotor. The proposed method for the fault identification is a hybrid technique which combined both the Kalman filter and Artificial Neural Network (ANN). Kalman filter recognized data from the system sensors and can indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. The information will then be fed to Artificial Neural Network (ANN), which consist of a bank of parameter estimation that generates the failure state. This Artificial Neural Network (ANN) is an algorithm that is used to determine the type of fault and the severity level as well as isolate the fault from the system. The ANN is designed based on the back-propagation technique so that it can be trained to generate output based on the data. Based on the result comparison of the residual signal before filter and after filter, the algorithm of FDI is able to identify parts of the system that experience failure and the fault can be solved immediately allowing the Quadrotor to be back to its normal operation. It is also capable to acknowledge the user on the parts of the system which experienced failure and can provide user with the best instructions or solutions for the situation. It is also capable to cater a safe landing.
机译:故障检测和识别(FDI)在复杂的多输入多输出(MIMO)非线性系统中监视,识别和查明系统故障的类型和位置。在本研究中,使用四旋翼机器人来代表一个复杂的系统。该研究的目的是构造和设计一种故障检测和隔离算法。该动态模型基于Quadrotor的第一个原理:螺旋桨模型及其作用力和力矩生成。 Quadrotor控制器的设计使其可以使用姿态控制(内环)和位置控制(外环)进行控制。 PD控制器以Phi,Theta,Psi,x,y和z为参考来调整Quadrotor的姿态和位置。提出的故障识别方法是一种结合了卡尔曼滤波器和人工神经网络(ANN)的混合技术。卡尔曼滤波器可以识别来自系统传感器的数据,并可以在传感器读数中指示系统故障。误差预测是基于故障的幅度和故障的发生时间。然后,该信息将被馈送到人工神经网络(ANN),该人工神经网络由生成故障状态的一组参数估计组成。该人工神经网络(ANN)是一种算法,用于确定故障的类型和严重性级别以及将故障与系统隔离。 ANN是基于反向传播技术设计的,因此可以对其进行训练以基于数据生成输出。基于滤波器之前和之后的残差信号的结果比较,FDI算法能够识别系统中遇到故障的部分,并且可以立即解决故障,从而使Quadrotor恢复其正常运行。它还能够在发生故障的系统部分上确认用户,并可以为用户提供针对这种情况的最佳说明或解决方案。它也能够满足安全着陆的要求。

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