Fault tolerant control for robot manipulators using neural network and second-order sliding mode observer

Authors:
Mien Van;Hee-Jun Kang
Affiliations:
Graduate School of Electrical Engineering, University of Ulsan, South Korea;School of Electrical Engineering, University of Ulsan, South Korea
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Year:
2013

Citing 3
Cited 0

Sliding mode observers for fault detection and isolation

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Neural-network-based robust fault diagnosis in robotic systems

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A stable neural network-based observer with application to flexible-joint manipulators

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Abstract

This paper investigates an algorithm for fault tolerant control of uncertain robot manipulator with only joint position measurement using neural network and second-order sliding mode observer. First, a neural network (NN) observer is designed to estimate the modeling uncertainties. Based on the obtained uncertainty estimation, a second-order sliding mode observer is then designed for two purposes: 1) Providing the velocity estimation, 2) providing the fault information that is used for fault detection, isolation and identification. Finally, a fault tolerant control scheme is proposed for compensating the effect of uncertainties and faults based on the fault estimation information. Computer simulation results on a PUMA560 industrial robot are shown to verify the effectiveness of the proposed strategy.