Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Shanghai Jiaotong University (Science) Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. SICILIANO B, SCIAVICCO L, VILLANI L, et al. Robotics: Modelling, planning and control [M]. London, UK: Springer-Verlag, 2009.

    Book  Google Scholar 

  2. SPONG M W. Modeling and control of elastic joint robots [J]. Journal of Dynamic Systems, Measurement, and Control, 1987, 109(4): 310–318.

    Article  Google Scholar 

  3. SPONG M W, KHORASANI K, KOKOTOVIC P V. An integral manifold approach to the feedback control of flexible joint robots [J]. IEEE Journal of Robotics and Automation, 1987, 3(4): 291–300.

    Article  Google Scholar 

  4. KHORASANI K. Nonlinear feedback control of flexible joint manipulators: A single link case study [J]. IEEE Transactions on Automatic Control, 1990, 35(10): 1145–1149.

    Article  Google Scholar 

  5. RUDERMAN M. Compensation of nonlinear torsion in flexible joint robots: Comparison of two approaches [J]. IEEE Transactions on Industrial Electronics, 2016, 63(9): 5744–5751.

    Article  Google Scholar 

  6. GUO C Q, GAO H B, NI F L, et al. A vibration suppression method for flexible joints manipulator based on trajectory optimization [C]//Proceedings of 2016 IEEE International Conference on Mechatronics and Automation. Harbin, China: IEEE, 2016: 338–343.

    Chapter  Google Scholar 

  7. NAIDU D S. Singular perturbation analysis of a flexible beam used in underwater exploration [J]. International Journal of Systems Science, 2011, 42(1): 183–194.

    Article  MathSciNet  Google Scholar 

  8. CHAOUI H, SICARD P. Adaptive neural network control of flexible-joint robotic manipulators with friction and disturbance [C]//IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society. Montreal, Quebec, Canada: IEEE, 2012: 2644–2649.

    Chapter  Google Scholar 

  9. MACNAB C J B. Improved output tracking of a flexible-joint arm using neural networks [J]. Neural Processing Letters, 2010, 32(2): 201–218.

    Article  Google Scholar 

  10. SUBUDHI B, MORRIS A S. Singular perturbation approach to trajectory tracking of flexible robot with joint elasticity [J]. International Journal of Systems Science, 2003, 34(3): 167–179.

    Article  MathSciNet  Google Scholar 

  11. KHORASANI K. Adaptive control of flexible-joint robots [C]//Proceedings of the 1991 IEEE International Conference on Robotics and Automation. Sacramento, California, USA: IEEE, 1991: 2127–2134.

    Chapter  Google Scholar 

  12. GE S S, POSTLETHWAITE I. Adaptive neural network controller design for flexible joint robots using singular perturbation technique [J]. Transactions of the Institute of Measurement and Control, 1995, 17(3): 120–131.

    Article  Google Scholar 

  13. MIAO Z Q, WANG Y N. Robust dynamic surface control of flexible joint robots using recurrent neural networks [J]. Journal of Control Theory and Applications, 2013, 11(2): 222–229.

    Article  MathSciNet  Google Scholar 

  14. YEN H M, LI T H S, CHANG Y C. Adaptive neural network based tracking control for electrically driven flexible-joint robots without velocity measurements [J]. Computers and Mathematics with Applications, 2012, 64(5): 1022–1032.

    Article  Google Scholar 

  15. LOZANO R, BROGLIATO B. Adaptive control of robot manipulators with flexible joints [J]. IEEE Transactions on Automatic Control, 1992, 37(2): 174–181.

    Article  MathSciNet  Google Scholar 

  16. FU Z J, XIE W F, HAN X, et al. Nonlinear systems identification and control via dynamic multitime scales neural networks [J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(11): 1814–1823.

    Article  Google Scholar 

  17. ZHENG D D, XIE W F, REN X M, et al. Identification and control for singularly perturbed systems using multitime-scale neural networks [J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(2): 321–333.

    Article  MathSciNet  Google Scholar 

  18. GHORBEL F, HUNG J Y, SPONG M W. Adaptive control of flexible-joint manipulators [J]. IEEE Control Systems Magazine, 1989, 9(7): 9–13.

    Article  Google Scholar 

  19. ETXEBARRIA V, SANZ A, LIZARRAGA I. Control of a lightweight flexible robotic arm using sliding modes [J]. International Journal of Advanced Robotic Systems, 2005, 2(2): 103–110.

    Article  Google Scholar 

  20. ZHENG D D, XIE W F, CHAI T, et al. Identification and trajectory tracking control of nonlinear singularly perturbed system [J]. IEEE Transactions on Industrial Electronics, 2017, 64(5): 3737–3747.

    Article  Google Scholar 

Download references

Author information

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)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pengcheng Li  (李鹏程)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenfang Xie  (谢文芳)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dan Li  (李 丹)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenfang Xie  (谢文芳).

Additional information

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Published:

  • Issue Date:

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

Key words

CLC number

Document code

Search

Navigation