Sliding Mode Control of Magnetic Levitation Systems Using Hybrid Extended Kalman Filter
Abstract
This paper presents an approach to control a magnetic levitation system with uncertainty in the dynamics and the measurements. First, Sliding Mode Controller (SMC) is applied to the magnetic levitation system. Then, Hybrid Extended Kalman Filter (HEKF) is used to increase the robustness of the magnetic levitation system to uncertainties. The efficiency of such combined control method is verified by simulation results and performance parameters.
Key words: Magnetic levitation system; Sliding mode control; Hybrid extended kalman filterKeywords
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[1] Rote , D. M., & Cai , Y. (2002). Review of Dynamic Stability of Repulsive-Force Maglev Suspension System. IEEE Trans. Magn., 38 (2), 1383–1390.
[2] Hajjaji, A. E., & Ouladsine, M. (2001). Modeling and Nonlinear Control of Magnetic Levitation Systems. IEEE Trans. Ind. Electron., 48 (4), 831–838.
[3] Ono, M., Koga, S., & Ohtsuki, H. (2002). Japan’s Superconducting Maglev Train. IEEE Instrum. Meas. Mag., 5 (1), 9–15.
[4] Allaire , P., & Sinha, A. (1998). Robust Sliding Mode Control of a Planar Rigid Rotor System on Magnetic Bearings (pp. 577–586). Proc. 6th International Symposium on Magnetic Bearings, (Massachusetts).
[5] Trumper, D. L., Olson, M., & Subrahmanyan, P. K. (1997). Linearizing Control of Magnetic Suspension Systems. IEEE Transactions on Control Systems Technology , (4), 427–438.
[6] Barie, W., & Chiasson, J. (1996). Linear and Nonlinear State-Space Controllers for Magnetic Levitation. International Journal of Systems Science, 27 (11), 1153–1163.
[7] Huang, C. M., Yen, J. Y., & Chen, M.-S. (2000). Adaptive Nonlinear Control of Repulsive Maglev Suspension Systems. Control Engineering Practice, 8 (5), 1357–1367.
[8] Yang , Z. J., & Tateish, M. (1998). Robust nonlinear control of a magnetic levitation system via back step-ping approach. Proc. 37th SICE Annual Conference (SICE ’98) (pp.1063–1066). Chiba, Japan.
[9] Fujita, M., Matsumura, F., & Uchida, K. (1990). Experiments on the H∞ disturbance attenuation control of a magnetic suspension system. Proc. 29th IEEE Conference on Decision and Control ( pp. 2773–2778), Hawaii.
[10] Fujita, M., Namerikawa, T., Matsumura, F., & Uchida, K. (1995). µ-synthesis of an Electromagnetic Suspension System. IEEE Trans. Automatic Control, 40(3), 530–536.
[11] Zhao, F., Loh, S. C., & May, J. A.( 1999). Phase-Space Nonlinear Control Toolbox: The Maglev Experience, 5th International Hybrid Systems. Workshop (P. Antsaklis, W. Kohn, M. Lemmon, A. Nerode, and S. Sastry, eds.). Lecture Notes in Computer Science, 1567, 429–444
[12] Chen, C. H. (2009). Nonlinear System Control Using Adaptive Neural Fuzzy Networks Based on a Modified Differential Evolution. IEEE Trans on, Systems, MAN, and Cybernetics-Part C: Applications and Reviews, 39(4), 459-473.
[13] Lin, F. J., Teng, L. T., & Shieh, P. H. (2005). Hybrid controller with recurrent neural network for magnetic levitation system. IEEE Trans. Magn., 41(7), 2260–2269.
[14] Li, T. H. S., Kuo, C.L., & Guo, N.R. (2007). Design of an EP-Based Fuzzy Sliding Mode Control for a Magnetic Ball Suspension System. Chaos, Solutions and Fractals, 33, 1523-1531.
[15] Slotine , J.J., & Li, W. (1990) .Applied Nonlinear Control. New Jersey: Prentice Hall.
[16] Cho, D., Kato, Y., & Spilman, D. (1993). Sliding Mode and Classical Controllers in Magnetic Levitation Systems. IEEE Control Systems Magazine, 13(1), 42–48.
[17] Buckner, G. D. (2001). Intelligent Bounds on Modeling Uncertainties: Applications to Sliding Mode Control of a Magnetic Levitation System. IEEE International Conference on Systems, Man, and Cybernetics, 1, 81–86.
[18]Lin, F. j., & Teng, L. T. (2007). Intelligent Sliding Mode Control Using RBFN for Magnetic Levitation System. IEEE Trans on Industrial Electronics, 54(3), 1752-1762.
[19] Moon, F. C. (1994). Superconducting Levitation: Applications to Bearings and Magnetic Transportation. New York: Wiley.
[20] Al-muthairi, N. F., & Zribi, M. (2004). Sliding Mode Control of a Magnetic Levitation System. Mathmatical Problems in Engineering, 2, 93-107.
[21] Welch,G., & Bishop, G.(2001). An introduction to the Kalman filter. Course 8. In Proceedings of the ACM SIGGRAPH Conference, Los Angeles.
[22] Papoulis, A. (1991). Probability, Random Variables and Stochastic Processes (Third Edition). Singapore: McGraw-hill.
[23] Grewal, S., & Andrews, P. (2001). Kalman Filtering Theory and Practice Using Matlab, (2nd Edition). Wiley.
[24] Simon, D. (2006). Optimal State Estimation Kalman, H∞, and Nonlinear Approaches. New Jersey: Wiley.
DOI: http://dx.doi.org/10.3968/j.est.1923847920110202.114
DOI (PDF): http://dx.doi.org/10.3968/g2087
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