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A Backstepping Control of LSM Drive Systems Using Adaptive Modified Recurrent Laguerre OPNNUO
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  • Journal title : Journal of Power Electronics
  • Volume 16, Issue 2,  2016, pp.598-609
  • Publisher : The Korean Institute of Power Electronics
  • DOI : 10.6113/JPE.2016.16.2.598
 Title & Authors
A Backstepping Control of LSM Drive Systems Using Adaptive Modified Recurrent Laguerre OPNNUO
Lin, Chih-Hong;
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 Abstract
The good control performance of permanent magnet linear synchronous motor (LSM) drive systems is difficult to achieve using linear controllers because of uncertainty effects, such as fictitious forces. A backstepping control system using adaptive modified recurrent Laguerre orthogonal polynomial neural network uncertainty observer (OPNNUO) is proposed to increase the robustness of LSM drive systems. First, a field-oriented mechanism is applied to formulate a dynamic equation for an LSM drive system. Second, 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. As the LSM drive system is prone to nonlinear and time-varying uncertainties, an adaptive modified recurrent Laguerre OPNNUO is proposed to estimate lumped uncertainties and thereby enhance the robustness of the LSM drive system. The on-line parameter training methodology of the modified recurrent Laguerre OPNN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre OPNN are derived to accelerate parameter convergence. Finally, the effectiveness of the proposed control system is verified by experimental results.
 Keywords
Backstepping control;Laguerre orthogonal polynomial neural network;Permanent magnet linear synchronous motor;
 Language
English
 Cited by
1.
Comparative dynamic control for continuously variable transmission with nonlinear uncertainty using blend amend recurrent Gegenbauer-functional-expansions neural network, Nonlinear Dynamics, 2016  crossref(new windwow)
2.
A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control, Journal of Power Electronics, 2016, 16, 4, 1438  crossref(new windwow)
 References
1.
I. Boldea and S. A. Nasar, Linear Electric Actuators and Generators, London: Cambridge University Press, 1997.

2.
T. Egami and T. Tsuchiya, “Disturbance suppression control with preview action of linear DC brushless motor,” IEEE Trans. Ind. Electron., Vol. 42, No. 5, pp. 494-500, Oct. 1995. crossref(new window)

3.
M. Sanada, S. Morimoto, and Y. Takeda, “Interior permanent magnet linear synchronous motor for high-performance drives,” IEEE Trans. Ind. Appl., Vol. 33, No. 5, pp. 966-972, Jul./Aug. 1997. crossref(new window)

4.
I. Kanellakopoulos, P. V. Kokotovic, and A. S. Morse, “Systematic design of adaptive controller for feedback linearizable system,” IEEE Trans. Autom. Contr., Vol. 36, No. 11, pp. 1241-1253, Nov. 1991. crossref(new window)

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. Autom. Contr., Vol. 45, No. 7, pp. 1334-1341, Jul. 2000. crossref(new window)

6.
M. N. Eskander, “Minimization of losses in permanent magnet synchronous motors using neural network,” Journal of Power Electronics, Vol. 2, No. 3, pp 220-229, Jul. 2002.

7.
A. F. Payam, M. N. Hashemnia, and J. Faiz, “Robust DTC control of doubly-fed induction machines based on input-output feedback linearization using recurrent neural networks,” Journal of Power Electronics, Vol. 11, No. 5, pp. 719-725, Sep. 2011. crossref(new window)

8.
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.

9.
C. H. Lin, "Novel adaptive recurrent Legendre neural network control for PMSM servo-drive electric scooter," J. Dynamic Systems, Measurement, and Control- Transactions of the ASME, Vol. 137 / 011010-1, 12 pages, 2015.

10.
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, 2015. crossref(new window)

11.
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, 17 pages, 2015.

12.
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.

13.
J. C. Patra, P. K. Meher, and G. Chakraborty, “Development of Laguerre neural-network-based intelligent sensors for wireless sensor networks,” IEEE Trans. Instrum. Meas., Vol. 60, No. 3, pp. 725-734, Mar.2011. crossref(new window)

14.
J. J. E. Slotine and W. Li, Applied Nonlinear Control, Englewood Cliffs, NJ: Prentice-Hall, 1991.

15.
J. Astrom and B. Wittenmark, Adaptive Control, New York: Addison-Wesley, 1995.

16.
C. C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic system control,” IEEE Trans. Neural Netw., Vol. 6, No. 1, pp.144-156, Jan. 1995. crossref(new window)

17.
C. H. Lin, “Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive,” Intl. J. Electrical Power and Energy Systems, Vol. 52, pp. 143-160, Nov. 2013. crossref(new window)

18.
F. L. Lewis, J. Campos and R. Selmic, Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities. SIAM Frontiers in Applied Mathematics, 2002.

19.
F. J. Lin and C. H. Lin, “On-line gain-tuning IP controller using RFNN,” IEEE Trans. Aerosp. Electron. Syst., Vol. 37, No. 2, pp. 655-670, Apr. 2001. crossref(new window)

20.
K. J. Astrom and T. Hagglund, PID Controller: Theory, Design, and Tuning, North Carolina: Instrument Society of America, Research Triangle Park, 1995

21.
T. Hagglund and K. J. Astrom, “Revisiting the Ziegler-Nichols tuning rules for PI control,” Asian J. Control, Vol. 4, No. 4, pp. 364-380, Dec. 2002. crossref(new window)

22.
T. Hagglund and K. J. Astrom, “Revisiting the Ziegler-Nichols tuning rules for PI control – Part II: The frequency response method,” Asian J. Control, Vol. 6, No. 4, pp. 469-482, Dec. 2004. crossref(new window)