Takagi-Sugeno fuzzy controller for a magnetic levitation system laboratory equipment

This paper presents the design and the implementation of two control solutions applied to a magnetic levitation system laboratory equipment. The first solution deals with a state feedback control structure to stabilize the controlled plant which is the inner control loop for the cascade control system structure proposed as the second solution. The second solution is based on a Takagi-Sugeno fuzzy controller in the outer control loop to ensure the zero steady-state control errors. Simulation and real time experimental results are included to illustrate the performance of the control systems and to validate the two control solutions in regulation and tracking.

[1]  Stefan Preitl,et al.  Modern Control Solutions for Mechatronic Servosystems . Comparative Case Studies , 2009 .

[2]  Kazuo Tanaka,et al.  Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach , 2008 .

[3]  P. Dostal,et al.  Self-tuning control of nonlinear servo system: Comparison of LQ and predictive approach , 2009, 2009 17th Mediterranean Conference on Control and Automation.

[4]  Juhng-Perng Su,et al.  Implementation of the State Feedback Control Scheme for a Magnetic Levitation System , 2007, 2007 2nd IEEE Conference on Industrial Electronics and Applications.

[5]  Ladislav Madarász,et al.  Automatic Adaptation of Fuzzy Controllers , 2005 .

[6]  Igor Skrjanc,et al.  Identification of dynamical systems with a robust interval fuzzy model , 2005, Autom..

[7]  S. Kovács,et al.  A Brief Survey and Comparison on Various Interpolation Based Fuzzy Reasoning Methods , 2006 .

[8]  Igor Skrjanc,et al.  Model-Reference Fuzzy Adaptive Control as a Framework for Nonlinear System Control , 2003, J. Intell. Robotic Syst..

[9]  Rolf Isermann,et al.  Mechatronic Systems: Fundamentals , 2003 .

[10]  Abdelfatah M. Mohamed,et al.  Variable structure control of a magnetic levitation system , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[11]  Sylvie Galichet,et al.  Fuzzy Feedback Linearizing Controller and Its Equivalence With the Fuzzy Nonlinear Internal Model Control Structure , 2007, Int. J. Appl. Math. Comput. Sci..

[12]  Kyoung Kwan Ahn,et al.  Inverse Double NARX Fuzzy Modeling for System Identification , 2010, IEEE/ASME Transactions on Mechatronics.

[13]  Mohammed Chadli,et al.  Implementation of a fuzzy logic control for a silo's level regulation in stone quarries , 2007 .

[14]  P. Bucek,et al.  Engineering methods and software support for control of Distributed Parameter Systems , 2009, 2009 7th Asian Control Conference.

[15]  Jyh-Horng Chou,et al.  Design of Optimal Controllers for Takagi–Sugeno Fuzzy-Model-Based Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[16]  Kevin Kok Wai Wong,et al.  Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[17]  Huachun Wu,et al.  Study on Fuzzy Control Algorithm for Magnetic Levitated Platform , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[18]  J. Vascák,et al.  Using Neural Gas Networks in Traffic Navigation , 2009 .

[19]  Rodolfo E. Haber,et al.  An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process , 2009, Appl. Soft Comput..

[20]  Claudia-Adina Dragos,et al.  Nonlinear and linearized models and low-cost control solution for an electromagnetic actuator , 2009, 2009 5th International Symposium on Applied Computational Intelligence and Informatics.