A Robust Fault Detection and Isolation Scheme for Robot Manipulators Based on Neural Networks
A Robust Fault Detection and Isolation Scheme for Robot Manipulators Based on Neural Networks
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DOI：
10.1007/978-3-642-24728-6_4
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
Mien Van;Hee-Jun Kang;Y. Ro
中科院分区：
文献类型：
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作者：
Mien Van;Hee-Jun Kang;Y. Ro
研究了一种基于神经网络的机器人鲁棒故障检测与隔离算法。利用两个神经网络：第一个NN（NN 1）用于再现机器人的动态行为，而第二个NN（NN 2）用于实现故障检测和隔离的在线近似。这种方法的重点是检测由于故障的机器人动力学的变化。在线监测不仅用于检测故障，而且用于提供故障特征的估计。一个两连杆机器人的计算机仿真实例表明了该算法在故障检测和隔离设计过程中的有效性。
This paper investigates an algorithm to the robust fault detection and isolation(FDI) in robot manipulators using Neural Networks(NNs). Two Neural Networks are utilized: the first NN (NN1) is employed to reproduce the robot’s dynamic behavior, while the second NN (NN2) is used to achieve the online approximation for fault detection and isolation. This approach focused on detecting changes in the robot dynamics due to faults. An online monitoring is used not only to detect faults but also to provide estimates of the fault characteristics. A computer simulation example for a two link robot manipulator shows the effectiveness of the proposed algorithm in the fault detection and isolation design process.