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A novel neural second-order sliding mode observer for robust fault diagnosis in robot manipulators

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Abstract

This paper investigates an algorithm for fault diagnosis in robot manipulators using a novel neural second-order sliding mode observer. Differently from the conventional neural network observer and first-order sliding mode observer for the robust fault estimation schemes, the second-order sliding mode observer is first designed and compared with them. Although the second-order sliding mode observer converges faster and with less error than each of the neural network and the first-order sliding mode observer does, it requires prior knowledge of the upper bound of the fault function. Because of this disadvantage, a neural second-order sliding mode observer is designed, which combines the second-order sliding mode observer with the neural network observer. The resulting observer not only preserves the features of the second-order sliding mode observer but also can improve it by removing the need for prior knowledge of the fault function upper bound. Computer simulation results for a PUMA560 industrial robot are also shown to verify the effectiveness of the proposed strategy.

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Abbreviations

FD:

Fault diagnosis

FDI:

Fault detection and isolation

NN:

Neural network

SM:

Sliding mode

SOSM:

Second-order sliding mode

NSOSM:

Neural second-order sliding mode

EOI:

Equivalent Output Injection

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Authors and Affiliations

  1. Graduate School of Electrical Engineering, University of Ulsan, Ulsan, South Korea, 680-749

    Mien Van

  2. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea, 680-749

    Hee-Jun Kang & Young-Soo Suh

Authors
  1. Mien Van
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  2. Hee-Jun Kang
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  3. Young-Soo Suh
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Correspondence to Hee-Jun Kang.

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Van, M., Kang, HJ. & Suh, YS. A novel neural second-order sliding mode observer for robust fault diagnosis in robot manipulators. Int. J. Precis. Eng. Manuf. 14, 397–406 (2013). https://doi.org/10.1007/s12541-013-0055-5

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  • DOI: https://doi.org/10.1007/s12541-013-0055-5

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