Skip to main content
SpringerLink
Log in

Neural Network Control of a Flexible Beam

  • Chapter
  • First Online:
Active Vibration Control and Stability Analysis of Flexible Beam Systems

  • 563 Accesses

Abstract

In recent years, control theory and method has been widely used in many fields and has been made great progress in the research of adaptive control [1,2,3]. Among the research methods, the adaptive control [4, 5] technology provides a powerful tool to solve the model uncertainty caused by the variety of parameters. The design of the adaptive controller is based on the identification of the system parameters. However, the identification and computation of complex system are time-consuming, which makes it difficult to realize real-time control of the fast system. In recent years, artificial intelligence [6], especially the research of neural network (NN) [7,8,9], has provided an effective method to solve these problems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. S.S. Ge, T.H. Lee, G. Zhu, Energy-based robust controller design for multi-link flexible robots. Mechatronics 6(7), 779–798 (1996)

    Article  Google Scholar 

  2. Y. Li, G. Liu, T. Hong, K. Liu, Robust control of a two-link flexible manipulator with neural networks based quasi-static deflection compensation, in 2003 Proceedings of the American Control Conference, vol. 6 (IEEE, 2003), pp. 5258–5263

    Google Scholar 

  3. S.K. Pradhan, B. Subudhi, Real-time adaptive control of a flexible manipulator using reinforcement learning. IEEE Trans. Autom. Sci. Eng. 9(2), 237–249 (2012)

    Article  Google Scholar 

  4. L. Tian, C. Collins, Adaptive neuro-fuzzy control of a flexible manipulator. Mechatronics 15(10), 1305–1320 (2005)

    Article  Google Scholar 

  5. W. He, S. Zhang, S.S. Ge, C. Liu, Adaptive boundary control for a class of inhomogeneous Timoshenko beam equations with constraints. IET Control Theory Appl. 8(14), 1285–1292 (2014)

    Article  MathSciNet  Google Scholar 

  6. J. Weng, J. Mcclelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, E. Thelen, Artificial intelligence. Autonomous mental development by robots and animals. Science 291(5504), 599–600 (2001)

    Article  Google Scholar 

  7. M.M. Polycarpou, Stable adaptive neural control scheme for nonlinear systems. IEEE Trans. Autom. Control 41(3), 447–451 (1996)

    Article  MathSciNet  Google Scholar 

  8. Y. Li, S. Qiang, X. Zhuang, O. Kaynak, Robust and adaptive backstepping control for nonlinear systems using RBF neural networks. IEEE Trans. Neural Netw. 15(3), 693–701 (2004)

    Article  Google Scholar 

  9. S.S. Ge, C.C. Hang, T. Zhang, Adaptive neural network control of nonlinear systems by state and output feedback. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 29(6), 818–828 (1999)

    Article  Google Scholar 

  10. K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1(1), 4–27 (1990)

    Article  Google Scholar 

  11. A.U. Levin, K.S. Narendra, Control of nonlinear dynamical systems using neural networks. ii. Observability, identification, and control. IEEE Trans. Neural Netw. 7(1), 30–42 (1996)

    Article  Google Scholar 

  12. T.H. Lee, C.J. Harris, Adaptive Neural Network Control of Robotic Manipulators, vol. 19 (World Scientific, Singapore, 1998)

    Google Scholar 

  13. C. Wang, D.J. Hill, Deterministic Learning Theory for Identification, Recognition, and Control (CRC Press, Boca Raton, 2009)

    Google Scholar 

  14. Y.J. Liu, C.L. Chen, G.X. Wen, S. Tong, Adaptive neural output feedback tracking control for a class of uncertain discrete-time nonlinear systems. IEEE Trans. Neural Netw. 22(7), 1162–7 (2011)

    Article  Google Scholar 

  15. S.L. Dai, C. Wang, M. Wang, Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 111 (2014)

    Article  Google Scholar 

  16. S.L. Dai, C. Wang, F. Luo, Identification and learning control of ocean surface ship using neural networks. IEEE Trans. Ind. Inform. 8(34), 801–810 (2012)

    Article  Google Scholar 

  17. W. He, S.S. Ge, B.V.E. How, Y.S. Choo, Dynamics and Control of Mechanical Systems in Offshore Engineering (Springer, London, 2014)

    Book  Google Scholar 

  18. R.R. Selmic, F.L. Lewis, Deadzone compensation in motion control systems using neural networks. IEEE Trans. Autom. Control 45(4), 602–613 (1998)

    Article  MathSciNet  Google Scholar 

  19. F. Sun, Z. Sun, Y. Zhu, W. Lu, Stable neuro-adaptive control for robots with the upper bound estimation on the neural approximation errors. J. Intell. Robot. Syst. 26(1), 91–100 (1999)

    Article  Google Scholar 

  20. R. Cui, B. Ren, S.S. Ge, Synchronised tracking control of multi-agent system with high-order dynamics. IET Control Theory Appl. 6(5), 603–614 (2012)

    Article  MathSciNet  Google Scholar 

  21. K.S. Narendra, S. Mukhopadhyay, Adaptive control using neural networks and approximate models. IEEE Trans. Neural Netw. 8(3), 475–85 (1997)

    Article  Google Scholar 

  22. M. Kawato, K. Furukawa, R. Suzuki, A hierarchical neural-network model for control and learning of voluntary movement. Biol. Cybern. 57(3), 169–185 (1987)

    Article  Google Scholar 

  23. Z. Xiong, Z. Jie, A batch-to-batch iterative optimal control strategy based on recurrent neural network models. J. Process Control 15(1), 11–21 (2005)

    Article  MathSciNet  Google Scholar 

  24. C. Guoping, H. Jiazhen, Assumed mode method of a rotating flexible beam. Acta Mechanica Sinica 37(1), 48–56 (2005)

    Google Scholar 

  25. D.S. Cho, N. Vladimir, T.M. Choi, Approximate natural vibration analysis of rectangular plates with openings using assumed mode method. Int. J. Nav. Archit. Ocean Eng. 5(3), 478–491 (2013)

    Article  Google Scholar 

  26. A. Yan, Frequency analysis of a rotating cantilever beam using assumed mode method with coupling effect. Mech. Based Design Struct. Mach. 34(1), 25–47 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

    Wei He

  2. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

    Jinkun Liu

Authors
  1. Wei He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinkun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei He .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Tsinghua University Press, Beijing and Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

He, W., Liu, J. (2019). Neural Network Control of a Flexible Beam. In: Active Vibration Control and Stability Analysis of Flexible Beam Systems. Springer, Singapore. https://doi.org/10.1007/978-981-10-7539-1_10

Download citation

Publish with us

Policies and ethics

Search

Navigation