Advertisement

Temperature Control of a Regasification System for LNG-fuelled Marine Engines Using Nonlinear Control Techniques

  • Gun-Baek So
  • Hyun-Sik Yi
  • Yung-Deug Son
  • Gang-Gyoo Jin
Regular Papers Intelligent Control and Applications
  • 17 Downloads

Abstract

This paper presents two nonlinear PID (NPID) controllers which control the glycol temperature of a regasification system for LNG-fuelled engines. The NPID controllers have a parallel structure of the three nonlinear P, I, D actions or the linear P, D actions and nonlinear I action. A nonlinear function is employed to scale the error as input of the integral and implemented as a Takagi-Sugeno (T-S) fuzzy model. The controller parameters are optimally tuned by using a genetic algorithm. Furthermore, the stability problem of the overall system is verified based on the circle criterion. A set of simulation works is carried out to validate the efficiency of the two proposed controllers.

Keywords

Circle criterion nonlinear PID controller temperature control T-S fuzzy model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    S. Kumar, H. Kwon, K. H. Choi, W. Lim, J. H. Cho, K. J. Tak, and L. L. Moon et al., “LNG: An eco-friendly cryogenic fuel for sustainable development,” Applied Energy, vol. 88, no. 12, pp. 4264–4273, December 2011.CrossRefGoogle Scholar
  2. [2]
    IMO, Green House Gas Emissions from Ships, Phase 1 Report, 2008.Google Scholar
  3. [3]
    MAN Diesel & Turbo, ME-GI Duel Fuel MAN B&W engines: A Technical, Operational and Cost-effective Solution for Ships Fuelled by Gas, 2012.Google Scholar
  4. [4]
    S. Jafarzadeh, N. Paltrinieri, I. B. Utne, and H. Ellingsen, “LNG-fuelled fishing vessels: A systems engineering approach,” Transportation Research Part D, vol. 50, pp. 202–222, January 2017.CrossRefGoogle Scholar
  5. [5]
    V. Vinaya Krishna, K. Ramkumar, and V. Alagesan, “Control of heat exchangers using model predictive controller,” Proc. of IEEE Int. Conf. on Advances In Engineering, Science And Management, pp. 242–246, March 2012.Google Scholar
  6. [6]
    M. Pandey, K. Ramkumar, and V. Alagesan, “Design of fuzzy logic controller for a cross flow shell and tube heatexchanger,” Proc. of IEEE Int. Conf. on Advances In Engineering, Science And Management, pp. 150–154, March 2012.Google Scholar
  7. [7]
    P. Sivakumar, D. Prabhakaran, and T. Kannadasan, “Temperature control of shell and tube heat exchanger by using intelligent controllers-case study,” Int. J. of Computational Engineering Research, vol. 2, no. 8, pp. 285–291, December 2012.Google Scholar
  8. [8]
    G. M. Sarabeevi and M. L. Beebi, “Temperature control of shell and tube heat exchanger system using internal model controllers,” Proc. of Int. Conf. on Next Generation Intelligent Systems (ICNGIS), February 2016.Google Scholar
  9. [9]
    M. Korkmaz, O. Aydogdu, and H. Dogan, “Design and performance comparison of variable parameter nonlinear PID controller and genetic algorithm based PID controller,” Proc. of 2012 IEEE Int. Symp. on Innovations in Intelligent Systems and Applications, pp. 1–5, July 2012.Google Scholar
  10. [10]
    B. M. Isayed and M. A. Hawwa, “A nonlinear PID control scheme for hard disk drive servo systems,” Proc. of Mediterranean Conf. on Control & Automation, pp. 1–6, June 2007.Google Scholar
  11. [11]
    H. Seraji, “A new class of nonlinear PID controllers,” Proc. of 5th IFAC Robot Control, pp. 65–71, September 1997.Google Scholar
  12. [12]
    G. Zaidner, S. Korotkin, E. Shteimberg, A. Ellenbogen, M. Arad, and Y. Cohen, “Nonlinear PID and its application in process control,” Proc. of IEEE 26th Convention of Electrical and Electronics Engineers, pp. 574–577, November 2010.Google Scholar
  13. [13]
    H. Afrianto, M. R. Tanshen, B. Munkhbayar, U. T. Suryo, H. S. Chung, and H. M. Jeong, “A numerical investigation on LNG flow and heat transfer characteristic in heat exchanger,” Int. J. of Heat and Mass Transfer, vol. 68, pp. 110–118, January 2014.CrossRefGoogle Scholar
  14. [14]
    A. O’Dwyer, Handbook of PI and PID Controller Tuning Rules, 2nd ed., Imperial College Press, London, 2006.CrossRefzbMATHGoogle Scholar
  15. [15]
    T. Takagi and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control,” IEEE Trans. Sys. Man and Cyber., vol. 15, no. 1, pp. 116–132, January 1985.CrossRefzbMATHGoogle Scholar
  16. [16]
    H. K. Khalil, Nonlinear Systems, 3rd ed., Prentice Hall, New Jersey, 2002.zbMATHGoogle Scholar
  17. [17]
    Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, NY., 1992.CrossRefzbMATHGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Gun-Baek So
    • 1
  • Hyun-Sik Yi
    • 2
  • Yung-Deug Son
    • 3
  • Gang-Gyoo Jin
    • 4
  1. 1.Department of Convergence Study on the OSTOST School, KMOUBusanKorea
  2. 2.Computing Systems Inc.DaejeonKorea
  3. 3.Department of Mechanical Facility Control EngineeringKorea University of Technology and EducationChungcheongnam-doKorea
  4. 4.Division of Control and Automation EngineeringKMOUBusanKorea

Personalised recommendations