Model predictive control solution for magnetic levitation systems

The paper presents aspects related to the design and implementation of a cascade control solution (CCS), which is tested on laboratory equipment meant for the position control of a ferromagnetic sphere in a Magnetic Levitation System with Two Electromagnets (MLS2EM). The nonlinear mathematical model (MM) of the MLS2EM is derived on the basis of the first principles equations with experimentally identified parameters. This MM is next linearized at two operating points. The CCS includes a state feedback controller in the inner loop and a model predictive controller in the outer loop. Experimental results are given in order to validate the proposed CCS and to illustrate the performance of the position control system expressed as very good dynamics and zero steady-state control error.

[1]  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).

[2]  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.

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

[4]  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).

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

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

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

[8]  Przemyslaw Ignaciuk,et al.  LQ Optimal Sliding Mode Supply Policy for Periodic Review Inventory Systems , 2010, IEEE Transactions on Automatic Control.

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

[10]  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.

[11]  Tadeusz Kaczorek Minimum energy control of fractional positive continuous-time linear systems with bounded inputs , 2013, 2013 18th International Conference on Methods & Models in Automation & Robotics (MMAR).

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

[13]  Milos Manic,et al.  Internet based neural network online simulation tool , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[14]  Adam Krzyzak,et al.  Non-parametric identification of dynamic non-linear systems by a Hermite Series Approach , 2001, Int. J. Syst. Sci..

[15]  Andreas Kugi,et al.  A novel robust position estimator for self-sensing magnetic levitation systems based on least squares identification , 2011 .

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

[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.  PI-Fuzzy controllers for integral plants to ensure robust stability , 2007, Inf. Sci..

[19]  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).

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

[21]  Jerzy Baranowski,et al.  Observer-based feedback for the magnetic levitation system , 2012 .

[22]  Eneko Osaba,et al.  AMCPA: A Population Metaheuristic With Adaptive Crossover Probability and Multi-Crossover Mechanism for Solving Combinatorial Optimization Problems , 2014 .

[23]  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).

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

[25]  Imre J. Rudas,et al.  Fixed Point Transformations-Based Approach in Adaptive Control of Smooth Systems , 2007, RoMoCo.