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Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks

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Abstract

This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function (RBF) neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation (PDE) model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov’s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.

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Acknowledgement

The authors would like to thank the Editor-in-Chief, the Associate Editor, and the anonymous reviewers for their constructive comments, which helped to improve the quality and presentation of this paper.

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Authors and Affiliations

  1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 1000190, China

    Hong-Jun Yang & Min Tan

Authors
  1. Hong-Jun Yang
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  2. Min Tan
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Corresponding author

Correspondence to Min Tan.

Additional information

This work was supported by National Natural Science Foundation of China (No. 61703402)

Recommended by Associate Editor-in-Chief Guo-Ping Liu

Hong-Jun Yang received the Ph.D. degree in School of Automation Science and Electrical Engineering, Beihang University, China in 2016. He is currently working in the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences as a postdoctoral researcher.

His research interests include adaptive control and flexible robot system.

Min Tan received the B. Sc. degree in control engineering from Tsinghua University, China in 1986, and the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, China in 1990. He is currently a professor in the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China.

His research interests include advanced robot control, multirobot systems, biomimetic robots, and manufacturing systems.

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Yang, HJ., Tan, M. Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks. Int. J. Autom. Comput. 15, 239–248 (2018). https://doi.org/10.1007/s11633-018-1122-2

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