ENG  Vol.2 No.2 , February 2010
Sliding Mode Control with Auto-Tuning Law for Maglev System
Abstract
This paper presents a control strategy for maglev system based on the sliding mode controller with auto-tuning law. The designed adaptive controller will replace the conventional sliding mode control (SMC) to eliminate the chattering resulting from the SMC. The stability of maglev system is ensured based on the Lyapunov theory. Simulation results verify the effectiveness of the proposed method. In addition, the advantages of the proposed controller are indicated in comparison with a traditional sliding mode controller.

Cite this paper
nullL. Zhang, Z. Zhang, Z. Long and A. Hao, "Sliding Mode Control with Auto-Tuning Law for Maglev System," Engineering, Vol. 2 No. 2, 2010, pp. 107-112. doi: 10.4236/eng.2010.22015.
References

[1]   Y. Yoshihide, F. Masaaki, T. Masao, et al, “The first HSST maglev commercial train in Japan,” MAGLEV 2004 Proceedings, pp. 76–85, 2004.

[2]   R. Goodall, “Dynamic and control requirements for EMS maglev suspension,” MAGLEV 2004 Proceedings, pp. 926–934, 2004.

[3]   H. Wang, J. Li, K. Zhang, “Non-resonant response, bifurcation and oscillation suppression of anon-autonomous system with delayed position feedback control,” Nonlinear Dynamic, Vol. 51, pp. 447–464, 2008.

[4]   L. Zhang, L. Huang, Z. Zhang, “Stability and Hopf bifurcation of the maglev system with delayed position and speed feedback control,” Nonlinear Dynamic, Vol. 57, pp. 197–207, 2009.

[5]   C. Feng, W. U. Zhu, “Stochastic optimal control of strongly nonlinear systems under wide-band random ex- citation with actuator saturation,” Acta Mechanica Solida Sinica, Vol. 21, No. 2, pp. 116–126, 2008.

[6]   N. Taher, “A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convexeconomic dispatch problem,” Applied Energy,doi:10.1016/j.apenergy.2009.05.016, 2009.

[7]   H. Tomohisa, M. Wassim, Y. Konstantin, “Neural network hybrid adaptive control for nonlinear uncertain impulsive dynamical systems,” Nonlinear Analysis: Hybrid Systems, Vol. 2, pp. 862–874, 2008.

[8]   V. Suplina, U. Shaked, “Robust H1 output-feedback control of systems with time-delay,” Systems and Control Letters, Vol. 57, No. 3, pp. 193–199, 2008.

[9]   B. Giorgio, F. Leonid, P. Alessandro, et al, “Modern sliding mode control theory,” Berlin, Spinker, 2008.

[10]   J. J. Slotine, W. Li, “Applied nonlinear control,” Englewood Cliffs (NJ), Prentice-Hall, 1991.

[11]   Y. J. Huang, T. C. Kuo, “Robust position control of DC servomechanism with output measurement noise,” Electrical Engineering, Vol. 88, pp. 223–338, 2006.

[12]   K. Furuta, “VSS type self-tuning control,” IEEE Transactions on Industrial Electronics, 40, pp. 37–44, 1993.

[13]   P. M. Lee, J. H. Oh, “Improvements on VSS type self-tuning control for a tracking controller,” IEEE Transactions on Industrial Electronics, Vol. 45, pp. 319– 325, 1998.

[14]   C. T. Chen, W. D. Chang, “A feedforward neural network with function shape autotuning,” Neural Networks, Vol. 9, No. 4, pp. 627–641, 1996.

[15]   W. D. Chang, R. C. Hwang, J. G. Hsieh, “Application of an auto-tuning neuron to sliding mode control,” IEEE Transactions on Systems, Man, and Cybernetics-Part C, Vol. 32, pp. 517–529, 2002.

[16]   T. C. Kuo, J. H. Ying, S. H. Chang, “Sliding mode control with self-tuning law for uncertain nonlinear systems,” ISA Transactions, Vol. 47, pp. 171–178, 2008.

[17]   X. Rong, ?. O. ?Umit, “Sliding mode control of a class of underactuated systems,” Automatica, Vol. 44, pp. 233–241, 2008.

[18]   X. Cui, K. G. Shin, “Direct control and coordination using neural networks,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 686–697, 1993.

[19]   C. D. Richard, H. B. Rober, “Modern control systems,” Addison Wesley Longman, 1993.

 
 
Top