ROBUST MODEL PREDICTIVE CONTROL FOR A DC GENERATOR TO DESIGN HIGH POWER CURRENT SOURCE

Published 31 July 2020 •  vol 13  •  no 7  • 


Authors:

 

Farideh Cheraghishami, Ferdowsi university, Mashhad, Iran
Naser Pariz, Ferdowsi university, Mashhad, Iran

Abstract:

 

Nowadays, high power current sources have many practical applications in the industrial processes. High power current sources are usually made by semi-conductors power circuits or special generators. These generators have complicated constructions, high prices and practical limitations. In contrary, common DC generators with low prices and simple structures have low speed response and high time constant. In this article, a high power current source is designed via a common DC generator. The parametric uncertainty of generator is modeled as the Norm- Bounded uncertainty. The advantage of this type of uncertainty in contrast to polytopic uncertainty is avoiding online computational burdens. In this research, an output current of generator is controlled using the robust model predictive controller. In this method, the robust stability is guaranteed by parameter-dependent lyapunov functions (PDLF). The control law based on PDLF is less conservative than that based on single Lyapunov function (SLF). The effectiveness of this algorithm is illustrated by the simulation results.

Keywords:

 

Robust Model Predictive Control, DC Generator, High Power Current Sources

References:

 

[1] Bimbahra, P.S(2000) .Electrical Machinery Theory,performance and Applivation,Dhanpat Rai Publishers.
[2] Rajput, R.K. (2007). Direct Current Machines ,Laxmi Publication.
[3] Mohan, N.( 1989). Power Electronics;Principles,Analysis And Design,John Wiely.
[4] Renquan, L& Shukui, X&Lin,X.A ,(2008). Robust Hinf optimal speed control of linear DC motor,27th Chinese control conference,Kunming,china,july 16-18.
[5] Nguyan, B. Ngo, H. RYU, J.(2009). Novel robust control Algorithm of DC motors ,6th international conference on Ubiquitous Robots and Ambient Inteligence.
[6] Campo, P. J., & Morari, M. (1987). Robust model predictive control. In Proceedings of the American Control Conference, Minneapolis, MN , 1021–1026.
[7] Kothare, M. V. ,& Balakrishnan, V.,& Morari, M.(1996). Robust constrained model predictive control using linear matrix inequalities,” Automatica, 32( 10), 1361–1379.
[8] Cuzzola, F. A.,& Geromel, J. C.,& Morari, M. (2002). An improved approach for constrained robust model predictive control, Automatica, vol. 38(7),1183–1189.
[9] Boyd, S.,& Ghaoui, L. E.,& Feron, E.,& Balakrishnan, V.(1994)Linear Matrix Inequalities in System and Control Theory. Philadelphia, PA: Studies in Applied Mathematics, SIAM.
[10] de Olivera, M. C.,& Bernussou, J.,& Geromel, J. C. (1999). A new discretetime robust stability condition, Syst. Control Letters, 37( 4)261–265.
[11] Wan, Z.,& Kothare, M. V. (2002). Robust output feedback model predictive control using off-line linear matrix inequalities,” J. of Process Control, 12(7) 763–774.
[12] L¨ofberg, J.(2004).YALMIP: A toolbox for modeling and optimization in MATLAB, in Proceedings of the CACSD Conference, Taiwan, 2004, pp.284–289. [Online]. Available: http://control.ee.ethz.ch/˜joloef/yalmip.php

Citations:

 

APA:
Cheraghishami, F., & Pariz, N. (2020). Robust Model Predictive Control for a DC Generator to Design High Power Current Source. International Journal of Control and Automation (IJCA), ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, 13(7), 11-20. doi: 10.33832/ijca.2020.13.7.02.

MLA:
Cheraghishami, Farideh, et al. “Robust Model Predictive Control for a DC Generator to Design High Power Current Source.” International Journal of Control and Automation, ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, vol. 13, no. 7, 2020, pp. 11-20. IJCA, http://article.nadiapub.com/IJCA/vol13_no7/2.html.

IEEE:
[1] F. Cheraghishami, and N. Pariz, "Robust Model Predictive Control for a DC Generator to Design High Power Current Source." International Journal of Control and Automation (IJCA), ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, vol. 13, no. 7, pp. 11-20, July 2020.