Model predictive controllers for magnetic levitation systems

The paper presents the design and the implementation of two model predictive controllers (MPCs) for magnetic levitation systems. The presentation is focused on the position control of a sphere in a magnetic levitation system with two electromagnets laboratory equipment in order to test the proposed controllers. A state feedback control structure is first designed to stabilize the system. In order to ensure the zero steady-state control errors, the second controller is based on an MPC in the outer control loop. The autoregressive exogenous model corresponding to closed-loop state feedback control system is computed in order to develop the MPC. The simulation results are included to illustrate the performance of the position control systems.

[1]  Hani Hagras,et al.  Analysis of the performances of type-1, self-tuning type-1 and interval type-2 fuzzy PID controllers on the Magnetic Levitation system , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[2]  Stefan Preitl,et al.  Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems , 2012, IEEE Transactions on Industrial Informatics.

[3]  Stefan Preitl,et al.  Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity , 2013, Inf. Sci..

[4]  Oscar Castillo,et al.  New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system , 2015, Inf. Sci..

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

[6]  Chin-Teng Lin,et al.  Nonlinear System Control Using Adaptive Neural Fuzzy Networks Based on a Modified Differential Evolution , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Stefan Preitl,et al.  Stability and Sensitivity Analysis of Fuzzy Control Systems. Mechatronics Applications , 2006 .

[8]  Emil M. Petriu,et al.  Experiment-Based Teaching in Advanced Control Engineering , 2011, IEEE Transactions on Education.

[9]  Lee Tong Heng,et al.  Applied Predictive Control , 2001 .

[10]  Ján Vaščák,et al.  Adaptation of Fuzzy Cognitive Maps for Navigation Purposes by Migration Algorithms , 2012 .

[11]  Chang Wu,et al.  Implicit generalized predictive control of an active magnetic bearing system , 2014, 2014 17th International Conference on Electrical Machines and Systems (ICEMS).

[12]  Shiqi An,et al.  Applying Simple Adaptive Control to Magnetic Levitation System , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[13]  Levente Kovács,et al.  Induced L2-norm minimization of glucose-insulin system for Type I diabetic patients , 2011, Comput. Methods Programs Biomed..

[14]  Mir Behrad Khamesee,et al.  Nonlinear controller design for a magnetic levitation device , 2007 .

[15]  Ahmad Jafarian,et al.  New Artificial Intelligence Approach for Solving Fuzzy Polynomial Equations , 2014 .

[16]  Claudio A Camasca,et al.  Bit-stream based model predictive controller for a magnetic levitation system , 2011, TENCON 2011 - 2011 IEEE Region 10 Conference.

[17]  Igor Skrjanc,et al.  Predictive Functional Control Based on Fuzzy Model: Design and Stability Study , 2005, J. Intell. Robotic Syst..

[18]  Stefan Preitl,et al.  Fuzzy controllers for tire slip control in anti-lock braking systems , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[19]  Claudia-Adina Dragos,et al.  Takagi-Sugeno fuzzy controller for a magnetic levitation system laboratory equipment , 2010, 2010 International Joint Conference on Computational Cybernetics and Technical Informatics.

[20]  Sumit Kumar Pandey,et al.  PID control of magnetic levitation system based on derivative filter , 2014, 2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD).

[21]  József K. Tar,et al.  On the design of an obstacle avoiding trajectory: Method and simulation , 2009, Math. Comput. Simul..

[22]  Ioan Dumitrache,et al.  INTELLIGENT TECHNIQUES FOR COGNITIVE MOBILE ROBOTS , 2004 .

[23]  Stefan Preitl,et al.  PI and PID controllers tuning for integral-type servo systems to ensure robust stability and controller robustness , 2006 .

[25]  Abdullah T. Elgammal,et al.  Fuzzy-based gain scheduling of Exact FeedForward Linearization control and sliding mode control for magnetic ball levitation system: A comparative study , 2014, 2014 IEEE International Conference on Automation, Quality and Testing, Robotics.

[26]  Claudia-Adina Dragos,et al.  Iterative performance improvement of fuzzy control systems for three tank systems , 2012, Expert Syst. Appl..

[27]  Bo Wang,et al.  Networked predictive control of magnetic levitation system , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[28]  Stefan Preitl,et al.  Iterative Feedback Tuning in Fuzzy Control Systems. Theory and Applications , 2006 .

[29]  Radu Calinescu,et al.  Large-scale complex IT systems , 2011, Commun. ACM.

[30]  Zsolt Csaba Johanyák,et al.  Fuzzy Modeling of Thermoplastic Composites' Melt Volume Rate , 2014, Comput. Informatics.

[31]  M. J. Nigam,et al.  Model Predictive Controller design and perturbation study for Magnetic Levitation System , 2014, 2014 Recent Advances in Engineering and Computational Sciences (RAECS).

[32]  Robert Piotrowski,et al.  A Model-Based Improved Control of Dissolved Oxygen Concentration in Sequencing Wastewater Batch Reactor , 2014 .

[33]  Joanna Zietkiewicz Constrained predictive control of a levitation system , 2011, 2011 16th International Conference on Methods & Models in Automation & Robotics.

[34]  B. K. Roy,et al.  Multiple model based predictive control of magnetic levitation system , 2014, 2014 Annual IEEE India Conference (INDICON).