Intelligent Sliding-Mode Control Using RBFN for Magnetic Levitation System

An intelligent sliding-mode control system using a radial basis function network (SMCRBFN) is proposed to control the position of a levitated object of a magnetic levitation system to compensate the uncertainties including the friction force in this study. First, the dynamic model of the magnetic levitation system is derived. Then, a sliding-mode approach is proposed to compensate the uncertainties that occurred in the magnetic levitation system. Moreover, to relax the requirement of uncertainty bound in the design of a traditional sliding-mode control system and further increase the robustness of the magnetic levitation system, a radial basis function network estimator is proposed to estimate the uncertainties of the system dynamics online. The effectiveness of the proposed control scheme is verified by some experimental results. With the proposed SMCRBFN system, the position of the levitated object of the magnetic levitation system possesses the advantages of good transient control performance and robustness to uncertainties for tracking periodic trajectories

[1]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[2]  M. Ono,et al.  Japan's superconducting Maglev train , 2002 .

[3]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[4]  D. L. Trumper,et al.  Linearizing control of magnetic suspension systems , 1997, IEEE Trans. Control. Syst. Technol..

[5]  G. D. Buckner.  Intelligent bounds on modeling uncertainty: applications to sliding mode control , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[6]  Jin-Ho Seo,et al.  Design and analysis of the nonlinear feedback linearizing control for an electromagnetic suspension system , 1996, IEEE Trans. Control. Syst. Technol..

[7]  Faa-Jeng Lin,et al.  Slider-crank mechanism control using adaptive computed torque technique , 1998 .

[8]  F. Moon,et al.  Superconducting levitation : applications to bearings and magnetic transportation , 1994 .

[9]  I. J. Sorkin,et al.  A Method of Using Audio Signal-to-Noise Measurements to Obtain Criterion Contours for the Probability Scoring Model for Scoring Voice Communications Reception , 1968 .

[10]  Kuo-Kai Shyu,et al.  A novel motor drive design for incremental motion system via sliding-mode control method , 2005, IEEE Transactions on Industrial Electronics.

[11]  Ronald G. Harley,et al.  MLP/RBF neural-networks-based online global model identification of synchronous generator , 2005, IEEE Transactions on Industrial Electronics.

[12]  D. M. Rote,et al.  A review of dynamic stability of repulsive-force maglev suspension systems , 1998 .

[13]  Derong Liu,et al.  Neural network-based model reference adaptive control system , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[15]  Li-Chen Fu,et al.  A novel dual-axis repulsive Maglev guiding system with permanent magnet: modeling and controller design , 2003 .

[16]  Faa-Jeng Lin,et al.  Robust Fuzzy Neural Network Sliding-Mode Control for Two-Axis Motion Control System , 2006, IEEE Transactions on Industrial Electronics.

[17]  Zi-Jiang Yang,et al.  Adaptive robust nonlinear control of a magnetic levitation system , 2001, Autom..

[18]  William Gerard Hurley,et al.  Electromagnetic design of a magnetic suspension system , 1997 .

[19]  Ahmed El Hajjaji,et al.  Modeling and nonlinear control of magnetic levitation systems , 2001, IEEE Trans. Ind. Electron..

[20]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[21]  Barry W. Williams,et al.  Sensorless second-order sliding-mode speed control of a voltage-fed induction-motor drive using nonlinear state feedback , 2005 .

[22]  Vadim I. Utkin,et al.  Sliding mode control design principles and applications to electric drives , 1993, IEEE Trans. Ind. Electron..

[23]  Faa-Jeng Lin,et al.  Hybrid controller with recurrent neural network for magnetic levitation system , 2005, IEEE Transactions on Magnetics.

[24]  Hassan K. Khalil,et al.  Output feedback control of nonlinear systems using RBF neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[25]  Chih-Lyang Hwang,et al.  Trajectory tracking of large-displacement piezoelectric actuators using a nonlinear observer-based variable structure control , 2005, IEEE Transactions on Control Systems Technology.

[26]  Paul I. Ro,et al.  Magnetic servo levitation by sliding-mode control of nonaffine systems with algebraic input invertibility , 2005, IEEE Transactions on Industrial Electronics.