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Identification and Control of Flexible Joint Robot Using Multi-Time-Scale Neural Network

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Abstract

In this paper, a new identification and control scheme for the flexible joint robotic manipulator is proposed. Firstly, by defining some new state variables, the commonly used dynamic equations of the flexible joint robotic manipulators are transformed into the standard form of a singularly perturbed model. Subsequently, an optimal bounded ellipsoid algorithm based identification scheme using multi-time-scale neural network is proposed to identify the unknown system dynamic equations. Lastly, by using the singular perturbation theory, an indirect adaptive controller based on the identified model is proposed to control the system such that the joint angles can track the given reference signals. The closed-loop stability of the whole system is proved, and the effectiveness of the proposed schemes is verified by simulations.

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Authors and Affiliations

  1. Department of Mechanical, Industrial & Aerospace Engineering, Concordia University, Montreal, H3G 1M8, Canada

    Dongdong Zheng  (郑冬冬), Pengcheng Li  (李鹏程) & Wenfang Xie  (谢文芳)

  2. College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Pengcheng Li  (李鹏程)

  3. Department of Electrical and Computer Engineering, Concordia University, Montreal, H3G 1M8, Canada

    Dan Li  (李 丹)

Authors
  1. Dongdong Zheng  (郑冬冬)
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  2. Pengcheng Li  (李鹏程)
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  3. Wenfang Xie  (谢文芳)
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  4. Dan Li  (李 丹)
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Corresponding author

Correspondence to Wenfang Xie  (谢文芳).

Additional information

Foundation item: the Natural Sciences and Engineering Research Council of Canada (No. N00892)

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Zheng, D., Li, P., Xie, W. et al. Identification and Control of Flexible Joint Robot Using Multi-Time-Scale Neural Network. J. Shanghai Jiaotong Univ. (Sci.) 25, 553–560 (2020). https://doi.org/10.1007/s12204-020-2210-3

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  • DOI: https://doi.org/10.1007/s12204-020-2210-3

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