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A hierarchical multiple-model approach for detection and isolation of robotic actuator faults

机译:用于检测和隔离机器人执行器故障的分层多模型方法

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Modern robotic systems perform elaborate tasks in a complicated environment and have close interactions with humans. Therefore fault detection and isolation (FDI) schemes must be carefully designed and implemented on robotic systems in order to guarantee safe and reliable operations. In this paper, we propose a hierarchical multiple-model FDI (HMM-FDI) scheme for robotic actuator faults. The proposed algorithm performs FDI in stages and refines the associated model set at each stage. Consequently only a small number of models are required for a wide class of faults, including abrupt faults, incipient faults, and simultaneous faults. Experiments are conducted to show that the HMM-FDI scheme can successfully and immediately detect and isolate various types of actuator faults.
机译:现代机器人系统在复杂的环境中执行复杂的任务,并且与人之间有着密切的互动。因此,必须仔细设计并在机器人系统上实施故障检测和隔离(FDI)方案,以确保安全可靠的运行。在本文中,我们提出了一种针对机器人执行器故障的分层多模型FDI(HMM-FDI)方案。所提出的算法分阶段执行FDI,并在每个阶段完善关联的模型集。因此,对于各种类型的故障(包括突然故障,初期故障和同时发生的故障),只需要少量模型。实验表明,HMM-FDI方案可以成功并立即检测并隔离各种类型的执行器故障。

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