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Stability analysis of reference compensation technique for controlling robot manipulators by neural network

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Abstract

Neural network control for robot manipulators is aimed to compensate for uncertainties in the robot dynamics. The location of a compensating point differentiates the control scheme into two categories, the feedback error learning (FEL) scheme and the reference compensation technique (RCT). The RCT scheme is relatively less used although it has several structural advantages. In this paper, the global stability of the RCT scheme is analyzed on the basis of Lyapunov function. The analysis turns out that the stability depends upon the magnitude of the controller gains. Simulation studies of controlling the position of a two-link robot manipulator are conducted.

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Authors and Affiliations

  1. Intelligent Systems and Emotional Engineering (ISEE) Laboratory, Department of Mechatronics Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Korea

    Seul Jung

Authors
  1. Seul Jung
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Correspondence to Seul Jung.

Additional information

Recommended by Associate Editor Xiaojie Su under the direction of Editor Fuchun Sun. This work has been supported in part by the 2014 National Research Foundation of Korea (NRF-2014R1A2A1A11049503).

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Jung, S. Stability analysis of reference compensation technique for controlling robot manipulators by neural network. Int. J. Control Autom. Syst. 15, 952–958 (2017). https://doi.org/10.1007/s12555-015-0070-7

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  • DOI: https://doi.org/10.1007/s12555-015-0070-7

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