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

Te-Sheng Hsiao*, Mao Chiao Weng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Modern robotic systems perform elaborate tasks in complicated environments 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 to detect and isolate actuator faults of robot manipulators. 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 to detect and isolate various types of unexpected actuator faults, including abrupt faults, incipient faults, and simultaneous faults. In addition, the computational load is alleviated due to the reduced-sized model set. The relation between the fault detection stage of the HMM-FDI scheme and the likelihood ratio test is explicitly revealed and theoretical upper bounds of the false alarm and missed detection probabilities are evaluated. Then we conduct experiments to demonstrate the ability of the HMM-FDI scheme in successful and immediate detection and isolation of actuator faults.

Original languageEnglish
Pages (from-to)154-166
Number of pages13
JournalRobotics and Autonomous Systems
Issue number2
StatePublished - 1 Feb 2012


  • Actuator faults
  • Fault detection
  • Fault isolation
  • GPB-2 algorithm
  • Multiple model
  • Robot manipulator
  • Unscented Kalman filter


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