Journal of Intelligent & Robotic Systems

, Volume 95, Issue 1, pp 137–147 | Cite as

Neural Network Based Adaptive Actuator Fault Detection Algorithm for Robot Manipulators

  • Chang Nho Cho
  • Ji Tae Hong
  • Hong Ju KimEmail author


In order to improve the reliability of robotic systems, various fault detection and isolation (FDI) algorithms have been proposed. However, most of these algorithms are model-based and thus, an accurate model of the robot is required although it is hard to obtain and often time-varying. Acceleration estimation is an additional challenge in dynamic model-based algorithms as it is hard to measure accurately in practice. In this study, a neural network based fault detection algorithm that does not require the use of physical robot model and acceleration is proposed. By utilizing neural network, the fault torque can be estimated, which allows effective fault detection and diagnosis. The feasibility of the proposed fault detection algorithm is validated through various simulations and experiments.


Fault detection Neural network Residual observer Robot safety 


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This research was supported by Korea Electrotechnology Research Institute(KERI) Primary research program through the National Research Council of Science & Technology(NST) funded by the Ministry of Science, ICT and Future Planning (MSIP) (No. 17-12-N0101-22)

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Precision Control Research CenterKorea Electrotechnology Research InstituteChangwon-siKorea

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