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Time Delay Compensation of a Robotic Arm based on Multiple Sensors for Indirect Teaching

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A Correction to this article was published on 23 September 2021

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Abstract

In this paper, a remote-control system for a six-degree-of-freedom robotic arm that uses an indirect teaching method is proposed. In the indirect teaching method, an essential time delay occurs, which degrades the system performance. To overcome this time delay, which can be modeled using a Smith predictor, a model neural network (MNN) has been adopted. The Smith predictor is a model-based algorithm that is uncertain and prone to interference. In this study, the MNN has been utilized in an effective manner to model the system to support the Smith predictor algorithm. Using this time delay compensation, the outer loop proportional, integral, and derivative (PID) control gains are adjusted in an optimal manner through a PID neural network (PIDNN) to ensure that the robotic arm follows human commands precisely. By using the PIDNN proposed in this paper, the time required for indirect teaching application of the robot arm can be reduced.

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Acknowledgements

This research is based on work supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under the Industrial Technology Innovation Program, No. 10073147.

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Authors and Affiliations

  1. Department of Electronics Engineering, Pusan National University, 63-2 Jangjeon-dong, Geumjeong-gu, Busan, 46241, Korea

    Xiaolu Zhang, Dongeon Kim, Jinuk Bang & Jangmyung Lee

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  1. Xiaolu Zhang
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  2. Dongeon Kim
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  3. Jinuk Bang
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  4. Jangmyung Lee
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Correspondence to Jangmyung Lee.

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The original online version of this article was revised: Due to an unfortunate oversight during the correction process the author name of Dongeun Kim has been given incorrect. Further the biography of Jinuk Bang has been given erroneously.

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Zhang, X., Kim, D., Bang, J. et al. Time Delay Compensation of a Robotic Arm based on Multiple Sensors for Indirect Teaching. Int. J. Precis. Eng. Manuf. 22, 1841–1851 (2021). https://doi.org/10.1007/s12541-021-00542-w

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  • DOI: https://doi.org/10.1007/s12541-021-00542-w

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