Fault detection and identification in Quadrotor system (Quadrotor robot)

C. Jing, Dwi Pebrianti
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引用次数: 1

Abstract

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.
四旋翼系统(四旋翼机器人)故障检测与识别
在复杂多输入多输出(MIMO)非线性系统中,故障检测与识别(FDI)是对系统故障的监测、识别和定位。本研究采用四旋翼机器人来代表一个复杂的系统。研究的目的是构建和设计一种故障检测与隔离算法。这个动态模型是基于四旋翼的第一原理:螺旋桨模型和它的力以及力矩的产生。四旋翼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控制器使用Phi, Theta, Psi, x, y和z作为参考来调整四旋翼的姿态和位置。提出了一种卡尔曼滤波与人工神经网络相结合的故障识别方法。卡尔曼滤波从系统传感器中识别数据,并能在传感器读数中提示系统故障。误差预测是基于故障的大小和故障发生的时间。然后将这些信息馈送到人工神经网络(ANN),该网络由一组参数估计组成,产生故障状态。人工神经网络(ANN)是一种用于确定故障类型和严重程度,并将故障从系统中隔离出来的算法。基于反向传播技术设计了人工神经网络,使其可以根据数据进行训练以产生输出。通过对滤波前和滤波后的残差信号进行结果比较,FDI算法能够识别出系统出现故障的部分,并能立即解决故障,使四旋翼飞行器恢复正常运行。它还能够识别系统中遇到故障的部分的用户,并为用户提供最佳的指导或解决方案。它还能够满足安全着陆的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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