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Neural Network Based Finite-Time Adaptive Backstepping Control of Flexible Joint Manipulators

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10639))

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Abstract

This paper proposes a finite-time adaptive backstepping control for an n-link flexible joint manipulator based on neural network approximation. In each recursive step, an adaptive virtual controller or practical controller is designed to guarantee that all the state errors can converge into a small region within a finite time. Besides, two simple neural networks are employed to approximate and compensate for the lumped uncertainties, and the finite time stability analysis is provided based on Lyapunov synthesis. Finally, simulation results show the effectiveness of the proposed method.

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References

  1. Li, Z., Yang, C., Tang, Y.: Decentralised adaptive fuzzy control of coordinated multiple mobile manipulators interacting with non-rigid environments. IET Control Theory Appl. 7(3), 397–410 (2013)

    Article  MathSciNet  Google Scholar 

  2. He, W., David, A.O., Yin, Z., et al.: Neural network control of a robotic manipulator with input deadzone and output constraint. IEEE Trans. Syst. Man Cybern. Syst. 46(6), 759–770 (2016)

    Article  Google Scholar 

  3. He, W., Amoateng, D.O., Yang, C., et al.: Adaptive neural network control of a robotic manipulator with unknown backlash-like hysteresis. IET Control Theory Appl. 11(4), 567–575 (2017)

    Article  MathSciNet  Google Scholar 

  4. Na, J., Chen, Q., Ren, X., et al.: Adaptive prescribed performance motion control of servo mechanisms with friction compensation. IEEE Trans. Industr. Electron. 61(1), 486–494 (2013)

    Article  Google Scholar 

  5. Liu, H., Tian, X., Wang, G., et al.: Finite-Time \({H_\infty }\) control for high-precision tracking in robotic manipulators using backstepping control. IEEE Trans. Industr. Electron. 63(9), 5501–5513 (2016)

    Article  Google Scholar 

  6. Wang, M., Yang, A.: Dynamic learning from adaptive neural control of robot manipulators with prescribed performance. IEEE Trans. Syst. Man Cybern. Syst. PP(99), 1–12 (2017)

    Google Scholar 

  7. Dai, S.L., Wang, M., et al.: Learning from adaptive neural output feedback control of robot manipulators. IFAC Proc. Vols. 46(20), 737–742 (2013)

    Article  Google Scholar 

  8. Wang, L., Chai, T., Zhai, L.: Neural-network-based terminal sliding-mode control of robotic manipulators including actuator dynamics. IEEE Trans. Industr. Electron. 56(9), 3296–3304 (2009)

    Article  Google Scholar 

  9. Hu, Q., Xu, L., Zhang, A.: Adaptive backstepping trajectory tracking control of robot manipulator. J. Franklin Inst. 349(3), 1087–1105 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  10. Ghorbel, F., Hung, J.Y., Spong, M.W.: Adaptive control of flexible joint manipulators. IEEE J. Mag. 9(7), 9–13 (1989)

    Google Scholar 

  11. Yoo, S.J.: Distributed adaptive containment control of networked flexible-joint robots using neural networks. Expert Syst. Appl. 41(2), 470–477 (2014)

    Article  Google Scholar 

  12. Li, Y., Tong, S., Li, T.: Adaptive fuzzy output feedback control for a single-link flexible robot manipulator driven DC motor via backstepping. Nonl. Anal. Real World Appl. 14(1), 483–494 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  13. Yoo, S.J., Park, J.B., Choi, Y.H.: Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach. IEEE Trans. Neural Networks 19(10), 1712–1726 (2008)

    Article  Google Scholar 

  14. Haimo, V.T.: Finite time controllers. Soc. Industr. Appl. Math. 24(4), 760–770 (1986)

    MATH  MathSciNet  Google Scholar 

  15. Zheng, J.F., Feng, Y., Zheng, X.M., et al.: Adaptive backstepping-based terminal-sliding-mode control for uncertain nonlinear systems. Control Theory Appl. Chin. 26(4), 410–414 (2009)

    Google Scholar 

  16. Zheng, X., Li, L., Zheng, J., et al.: Non-singular terminal sliding mode backstepping control for the uncertain chaotic systems. In: 2nd International Symposium on Systems and Control in Aerospace and Astronautics, pp. 1–5. IEEE Press, Shenzhen (2008)

    Google Scholar 

  17. Tang, X., Chen, Q., Nan, Y., et al.: Backstepping funnel control for prescribed performance of robotic manipulators with unknown dead zone. In: The 27th Chinese Control and Decision Conference, pp. 1508–1513. IEEE Press, Qingdao (2015)

    Google Scholar 

  18. Lu, K., Xia, Y., Yu, C., et al.: Finite-time tracking control of rigid spacecraft under actuator saturations and faults. IEEE Trans. Autom. Sci. Eng. 13(1), 368–381 (2016)

    Article  Google Scholar 

  19. Yu, S., Yu, X., Shirinzadeh, B., Man, Z.: Continuous finite-time control for robotic manipulators with terminal sliding mode. Automatica 41, 1957–1964 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  20. Modares, H., Ranatunga, I., Lewis, F.L., et al.: Optimized assistive human-robot interaction using reinforcement learning. IEEE Trans. Cybern. 46(3), 655–667 (2016)

    Article  Google Scholar 

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Acknowledgments

The authors would thank the support from the National Natural Science Foundation (NNSF) of China under Grant No. 61573320, No. 61473262 and No. 61403343, and Zhejiang Provincial Natural Science Foundation under Grant No. Y17F030063.

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Authors and Affiliations

  1. Data Driven Intelligent Systems Laboratory, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

    Qiang Chen, Huihui Shi & Mingxuan Sun

Authors
  1. Qiang Chen
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  2. Huihui Shi
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  3. Mingxuan Sun
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Corresponding author

Correspondence to Mingxuan Sun .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Chen, Q., Shi, H., Sun, M. (2017). Neural Network Based Finite-Time Adaptive Backstepping Control of Flexible Joint Manipulators. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_43

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  • DOI: https://doi.org/10.1007/978-3-319-70136-3_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70135-6

  • Online ISBN: 978-3-319-70136-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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