INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.6, No.2, Jan. 2014

Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach

Full Text (PDF, 1157KB), PP.1-13


Views:131   Downloads:4

Author(s)

Morteza Sarailoo, Zahra Rahmani, Behrooz Rezaie

Index Terms

Step-Down DC-DC Converter, Hybrid Model, Mixed Logical Dynamical Model, Model Predictive Control, Fuzzy Supervisor

Abstract

In this paper, a fuzzy predictive control scheme is proposed for controlling output voltage of a step-down DC-DC converter in presence of disturbance and uncertainty. The DC-DC converter is considered as a hybrid system and modeled by Mixed Logical Dynamical modeling approach. The main objective of the paper is to design a Fuzzy Predictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. A model predictive control is designed based on the hybrid model and applied to the system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from lack of robustness in presence of disturbance and uncertainty. So, to succeed in facing up to the problem a fuzzy supervisor is utilized to adjust the main predictive controller based on the measured states of the system. In this paper it is shown that the proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in normal operation and robustness in presence of disturbance and uncertainty. Through simulations effectiveness of the proposed method is shown.

Cite This Paper

Morteza Sarailoo, Zahra Rahmani, Behrooz Rezaie,"Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.2, pp.1-13, 2014. DOI: 10.5815/ijisa.2014.02.01

Reference

[1]J. Kennedy, R. C. Eberhart. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks IV, IEEE Press, Piscataway, NJ (1995), pp.1942–1948.

[2]Geyer T, Papafotiou G, Morari M. Hybrid model predictive control of the step-down DC–DC Converter. IEEE T Cont Syst Tech, 2008, 16: 1112~1124.

[3]Middlebrook RD, Cuk S. A general unified approach to modeling switching power cnverter stages. In: IEEE Power Electronics Specialists Conference, June, 1976, Cleveland, Ohio, USA: 18~34.

[4]Kassakian JG, Schlecht MF, Verghese GC. Principles of power electronics. 1st ed. USA, Addison-Wesley, 1991.

[5]Hamill DC, Deane JHB, Jefferies D. Modeling of chaotic dc-dc converters by Iterated nonlinear mappings. IEEE T Power Electron, 1992, 7: 25~36.

[6] Mohan N, Undeland TM, Robbins WP. Power electronics: converters, applications and design. 1st ed. USA, Wiley, 1989.

[7]Hsien C, Sheng L, Lee R, Chen K. Analysis and implementation of a dc-dc step-down converter for low output-voltage and high output-current applications. In: IEEE International Symposium on Circuits and Systems, June, 2010, Paris, France: 3697~3700.

[8]Mazouz N, Midoun A. Control of a dc-dc converter by fuzzy controller for a solar pumping system. Int J Electr Power Energy Syst, 2011, 33: 1623~1630.

[9]Ismail EH. Large step-down dc–dc converters with reduced current stress. Energy Conver and Manag J, 2009, 50: 232~239.

[10]Mariethoz S, Almer S, Baja M, Beccuti AG, Patino D, Wernrud A, Buisson J, Cormerais H, Geyer T, Fujioka H, et al. Comparison of hybrid control techniques for buck and boost dc-dc converters. IEEE T contr syst, 2010, 18:1126~1145.

[11]Beccui AG. Explicit model predictive control of dc-dc switched mode power supplies with extended Kalman filtering. IEEE T Ind Electron, 2009, 56: 1864~1874.

[12]Diaz NL, Soriano JJ. Study of two control strategies based in fuzzy logic and artificial neural network compared with an optimal control strategy applied to a buck converter. In: Annual Meeting of the North American Fuzzy Information Processing Society, June, 2007, San Diego, CA, USA: 313~318.

[13]Erickson RW, Cuk S, Middlebrook RD. Large signal modeling and analysis of switching regulators. In: IEEE Power Electronics Specialists Conference, Aug, 1994, Barcelona, Spain: 240~250.

[14]Kostakis GT, Manias SN, Margaris NI. A generalized method for calculating the RMS values of switching power converters. IEEE T Power Electron, 2000, 15: 616~625.

[15]Camacho EF, Ramirez DR, Limon D, Munoz D, Alamo T. Model predictive control techniques for hybrid systems. Annu. Rev. in Control, 2010, 34: 21~31.

[16]Antsaklis PG, Koutsoukos XD. Model predictive control past present and future. Comput Chem Eng, 1999, 23: 667~682.

[17]Bemporad A, Morari M. Control of systems integrating logic, dynamics, and constraints. Automatica, 1999, 35: 407~427.

[18]Torrisi FD, Bemporad A. HYSDEL-a tool for generating computational hybrid models for analysis and synthesis problems. IEEE Trans. on Control Syst. Technol. 2004, 12: 235~249.

[19]Takagi T, Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Trans. on Man Cyber. 1985, 15: 116~132.

[20]Ershadi MH, Poudeh MB, Eshtehardiha S. Fuzzy Logic Controller Based Genetic Algorithm on the Step-down Converter. In: International Conference on Smart Manufacturing Application, April, 2008, Goyang-Si, South Korea: 324~328. 

[21]Mojica E, Quijano N. A replicator dynamics weighted control technique for a dc-dc converter. In: IEEE Congress on Evolutionary Computation, June, 2011, Ritz Carlton, New Orleans, USA: 1872~1878.

[22]Ramirez H. Nonlinear P-I controller design for switch mode dc-dc Power converters. IEEE Trans. on Circ. Syst. I, 1991, 38: 410~417.

[23]Lazar M, Keyser R. Nonlinear predictive control of a dc-to-dc converter. In: International Symposium on Power Electronics, Electrical Drives, Automation and Motion, June, 2004, Capri, Italy: 1~5.

[24]Clarke DW, Mohtadi C, Tuffs PS. Generalized predictive control : the basic algorithm. Automatica, 1987, 23: 149~160.

[25]Bemporad A. Hybrid Toolbox v.1.2.6. http://control.ee.ethz.ch/~hybrid/

[26]Nagelkerke NJD. A note on a general definition of the coefficient of determination. Biometrika, 1991, 78: 691~692.

[27]Sarailoo M, Rahmani Z and Rezaie B. Modeling of Three-Tank System with Nonlinear Valves Based on Hybrid System Approach. J. of Control Eng. and Technol. 2013, 3: 20~23.

[28]Sarailoo M.; Rahmani Z.; Rezaie B. MLD Model of Boiler-Turbine System Based on PWA Linearization Approach, International J. of Control Science and Engineering, 2012, 2: 88~92.

[29]Lawrnczuk M. On improving accuracy of computationally efficient nonlinear predictive control based on neural models. Appl. Soft Comput. 2011, 66: 5253~5267.

[30]Gerkšič S and Pregelj B. Tuning of a tracking multi-parametric predictive controller using local linear analysis. IET Control Theory Appl. 2012, 6: 669~679.

[31]Huang H, Li D and Xi Y. Design and input-to-state practically stable analysis of the mixed H2/H feedback robust model predictive control. IET Control Theory Appl. 2012, 6: 498~505.

[32]Wang Y, Zhang Y, Sun F, et al. Using subset sequence to approach the maximal terminal region for model predictive control. IET Control Theory Appl. 2012, 6: 596~601.

[33]Makhorin A. Glpk (gnu linear programming kit), 2007, version 4.42.