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RETRACTED ARTICLE: Nonlinear backstepping control design of LSM drive system using adaptive modified recurrent Laguerre orthogonal polynomial neural network

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This article was retracted on 15 February 2021

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Abstract

The good control performance of the permanent magnet linear synchronous motor (LSM) drive system is very difficult to achieve using linear controller because of uncertainty effects, such as ending-fictitious force. A backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. Although favorable tracking responses can be obtained by the backstepping control system, the chattering in the control effort is critical because of the large control gain. Because there are many nonlinear and time-varying uncertainties in the LSM drive systems, the nonlinear backsteping control system, which an adaptive modified recurrent Laguerre orthogonal polynomial neural network (NN) is used to estimate uncertainty, is thus proposed to reduce the chattering in the control effort and thereby enhance the robustness of the LSM drive system. In addition, the on-line parameter training methodology of the modified recurrent Laguerre orthogonal polynomial NN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre orthogonal polynomial NN are derived to accelerate parameter convergence. Finally, comparison of the experimental results of the present study demonstrates the high control performance of the proposed control scheme.

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References

  1. I. Boldea and S. A. Nasar, Linear Electric Actuators and Generators, Cambridge University Press, London, 1997.

    Book  Google Scholar 

  2. T. Egami and T. Tsuchiya, “Disturbance suppression control with preview action of linear DC brushless motor,” IEEE Trans. Industrial Electronics, vol. 42, no. 5, pp. 494–500, Oct. 1995.

    Article  Google Scholar 

  3. M. Sanada, S. Morimoto, and Y. Takeda, “Interior permanent magnet linear synchronous motor for highperformance drives,” IEEE Trans. Industry Applications, vol. 33, no. 5, pp. 966–972, July/Aug. 1997.

    Article  Google Scholar 

  4. I. Kanellakopoulos, P. V. Kokotovic, and A. S. Morse, “Systematic design of adaptive controller for feedback linearizable system,” IEEE Trans. Automatic Control, vol. 36, no. 11, pp. 1241–1253, Nov. 1991.

    Article  MathSciNet  Google Scholar 

  5. G. Bartolini, A. Ferrara, L. Giacomini, and E. Usai, “Peoperties of a combined adaptive/second-order sliding mode control algorithm for some classes of uncertain nonlinear systems,” IEEE Trans. Automatic Control, vol. 45, no. 7, pp. 1334–1341, July 2000.

    Article  MathSciNet  Google Scholar 

  6. M. Krstic, P. V. Kokotovic, and I. Kanellakopoulos, Nonlinear and Adaptive Control Design, John Wiley & Sons Inc., NJ, 1995.

    MATH  Google Scholar 

  7. B. Xu, C. Yang, and Y. Pan, “Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle,” IEEE Trans. Neural Networks and Learning Systems, vol. 26, no. 10, 2563-2575, Oct. 2015.

    Google Scholar 

  8. B. Xu, “Robust adaptive neural control of flexible hypersonic flight vehicle with dead-zone input nonlinearity,” Nonlinear Dynamics, vol. 80, no. 3, pp. 1509–1520, March 2015. [click]

    Article  MathSciNet  Google Scholar 

  9. D. Won and W. Kim, “Disturbance observer based backstepping for position control of Electro-hydraulic Systems,” International Journal of Control, Automation, and Systems, vol. 13, no. 2, pp. 488–493, April 2015. [click]

    Article  Google Scholar 

  10. L. Zuo, W. Yan, R. Cui, and J. Gao, “A coverage algorithm for multiple autonomous surface vehicles in flowing environments,” International Journal of Control, Automation, and Systems, vol. 14, no. 2, pp. 540–548, April 2016. [click]

    Article  Google Scholar 

  11. B. Xu, Z. Shi, C. Yang, and F. Sun, “Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form,” IEEE Trans. Cybernetics, vol. 44, no. 12, pp. 2626–2634, 2014.

    Article  Google Scholar 

  12. B. Xu, C. Yang, and Z. Shi, “Reinforcement learning output feedback NN control using deterministic learning technique,” IEEE Trans. Neural Networks and Learning Systems, vol. 25, no. 3, pp. 635–641, March 2014.

    Article  Google Scholar 

  13. S. J. Chang, J. Y. Lee, J. B. Park, and Y. H. Chow, “An online fault tolerant actor-critic neuro-control for a class of nonlinear systems using neural network HJB approach,” International Journal of Control, Automation, and Systems, vol. 13, no. 2, pp. 311–318, 2015. [click]

    Article  Google Scholar 

  14. D. Bu, W. Sun, H. Yu, C. Wang, and H. Zhang, “Adaptive robust control based on RBF neural networks for duct cleaning robot,” International Journal of Control, Automation, and Systems, vol. 13, no. 2, pp. 475–487, 2015. [click]

    Article  Google Scholar 

  15. C. H. Lin, “A PMSM driven electric scooter system with V-belt continuously variable transmission using novel hybrid modified recurrent Legendre neural network control,” Journal of Power Electronics, vol. 14, no. 5, pp 220–229, Sep. 2014.

    Google Scholar 

  16. C. H. Lin “Hybrid recurrent wavelet neural network control of PMSM servo-drive system for electric scooter,” International Journal of Control, Automation and Systems, vol. 12, no. 1, pp. 177–187, Feb. 2014. [click]

    Article  Google Scholar 

  17. C. H. Lin, “Dynamic control of V-belt continuously variable transmission-driven electric scooter using hybrid modified recurrent Legendre neural network control system,” Nonlinear Dynamics, vol. 79, no. 2, pp. 787–808, Jan. 2015. [click]

    Article  Google Scholar 

  18. C. H. Lin, “Hybrid recurrent Laguerre-orthogonalpolynomial NN control system applied in V-belt continuously variable transmission system using particle swarm optimization,” Mathematical Problems in Engineering, vol. 2015, Article ID 106707, 2015. [click]

    Google Scholar 

  19. C. H. Lin, “Composite recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization,” Nonlinear Dynamics, vol. 81, no. 3, pp. 1219–1245, Aug. 2015. [click]

    Article  Google Scholar 

  20. C. H. Lin, “Design of a composite recurrent Laguerre orthogonal polynomial neural network control with ameliorated particle swarm optimization for a continuously variable transmission system,” Control Engineering Practice, vol. 49, pp. 42–59, April 2016. [click]

    Article  Google Scholar 

  21. J. C. Patra, C. Bornand, and P. K. Meher, “Laguerre neural network-based smart sensors for wireless sensor networks,” IEEE Instrumentation and Measurement Technology Conference, pp. 832–837, 2009.

    Google Scholar 

  22. J. C. Patra, P. K. Meher, and G. Chakraborty, “Development of Laguerre neural-network-based intelligent sensors for wireless sensor networks,” IEEE Trans. on Instrumentation and Measurement, vol. 60, no. 3, pp. 725–734, March 2011.

    Article  Google Scholar 

  23. J. J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice-Hall, Englewood Cliffs, NJ, 1991.

    MATH  Google Scholar 

  24. J. Astrom, and B. Wittenmark, Adaptive Control, Addison-Wesley, New York, 1995.

    MATH  Google Scholar 

  25. C. C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic system control,” IEEE Trans. Neural Networks, vol. 6, no. 1, pp. 144–156, Jan. 1995.

    Article  Google Scholar 

  26. C. H. Lin, “Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive,” International Journal of Electrical Power and Energy Systems, vol. 52, pp. 143–160, Nov. 2013. [click]

    Article  Google Scholar 

  27. F. L. Lewis, J. Campos, and R. Selmic, Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities. SIAM Frontiers in Applied Mathematics, 2002.

    Book  Google Scholar 

  28. K. J. Astrom and T. Hagglund, PID Controller: Theory, Design, and Tuning, Instrument Society of America, Research Triangle Park, North Carolina, 1995.

    Google Scholar 

  29. T. Hagglund and K. J. Astrom, “Revisiting the Ziegler-Nichols tuning rules for PI control,” Asian Journal of Control, vol. 4, no. 4, pp. 364–380, Dec. 2002. [click]

    Article  Google Scholar 

  30. T. Hagglund and K. J. Astrom, “Revisiting the Ziegler-Nichols tuning rules for PI control-part II: the frequency response method,” Asian Journal of Control, vol. 6, no. 4, pp. 469–482, Dec. 2004. [click]

    Article  Google Scholar 

  31. F. J. Lin and C. H. Lin, “On-line gain-tuning IP controller using RFNN,” IEEE Trans. Aerospace and Electronics Systems, vol. 37, no. 2, pp. 655–670, April 2001.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, National United University, No. 2, Lienda, Nan-Shi Li, Miaoli, 36063, Taiwan, Republic of China

    Chih-Hong Lin

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  1. Chih-Hong Lin
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Correspondence to Chih-Hong Lin.

Additional information

Recommended by Associate Editor Sing Kiong Nguang under the direction of Editor Ju Hyun Park. This work was supported by the Ministry of Science and Technology Grant MOST 104-2221-E-239-011 in Taiwan, R.O.C. The author gratefully acknowledges financial support from Ministry of Science and Technology grant MOST 104-2221-E-239-011 in Taiwan, ROC.

Chih-Hong Lin received the B.S. and M.S. degrees in Electrical Engineering from National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C., and the Ph.D. degree in Electrical Engineering from Chung Yuan Christian University, Chung Li, Taiwan, R.O.C., in 1989, 1991, and 2001, respectively. He is currently a Professor in Electrical Engineering, National United University, Miaoli, Taiwan, R.O.C. His research interests include power electronics, motor servo drives and intelligent control.

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Lin, CH. RETRACTED ARTICLE: Nonlinear backstepping control design of LSM drive system using adaptive modified recurrent Laguerre orthogonal polynomial neural network. Int. J. Control Autom. Syst. 15, 905–917 (2017). https://doi.org/10.1007/s12555-015-0401-8

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  • DOI: https://doi.org/10.1007/s12555-015-0401-8

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