Research Article
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Year 2019, Volume: 3 Issue: 3, 175 - 181, 15.12.2019
https://doi.org/10.35860/iarej.535552

Abstract

References

  • 1. Morari, M. and J. H. Lee, Model predictive control: Past, present, and future. Comp. Chem. Eng., 1999. 23: p. 667-682.
  • 2. Kavery, Lal., S. Rakesh Kumar and R. Valarmathi, Model predictive control for MIMO process, ARPN Journal of Engineering and Applied Sciences, April 2018. 13(7): pp. 2666-2670.
  • 3. Gireesh, K. J. and S. Veena, Model Predictive Controller design for performance study of coupled tank process, ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE), 2013. 1(3): pp. 70-74.
  • 4. A. Afram, A. and F. Janabi-Sharifi, Theory and applications of HVAC control systems – A review of model predictive control (MPC), Building and Environment, 2014. 72: pp. 343-355.
  • 5. Rawlings, J. B., E. S. Meadows and K. R. Muske, Nonlinear model predictive control: A tutorial and survey. In IFAC ADCHEM'94, Kyoto, Japan, 1994. p.185-197.
  • 6. Qin, S. J and T. A. Badgwell, An overview of industrial model predictive technology. In Chemical Process Control V, Tahoe City, CA, 1997. p. 232-256.
  • 7. Richalet, J., Industrial applications of model based predictive control, Automatica, 1993. 29: p. 1251-1274.
  • 8. Abu-Ayyad, M. and R. Dubay, Real-time comparison of a number of predictive controllers, ISA Transactions, 2007. 46: p. 411-418.
  • 9. Holkar, K. S. and L. M. Waghmare, Discrete Model Predictive Control for DC drive Using Orthonormal Basis function, UKACC International Conference on control, Coventry, UK, 2010. p. 435-440.
  • 10. Mohanty, S., Artificial neural network based system identification and model predictive control of a flotation column, Journal of Process Control, 2009. 19: p. 991–999.
  • 11. Camacho, E. F. and C. Bordons, Model Predictive Control in the process industry, 1995, Springer.
  • 12. Camacho, E. F., Constrained generalized predictive control, IEEE Trans. Automat. Contr. , 1993. 38: p. 327-332.
  • 13. Rossiter, J. A. and B. Kouvaritakis, Constrained stable generalized predictive control, IEE Proc., Pt.D, 140, 1993. 4: p. 243-254.
  • 14. Kothare, M. V., V. Balakrishnan and M. Morari, Robust constrained model predictive control using linear matrix inequalities, Automatica, 1996. 32(10): p.1361-1379.
  • 15. Zidane, Z., Unconstrained and Constrained Predictive Control for the Multivariable Process with Non-minimum Phase, Journal of Modeling and Simulation of Materials, 2019. 2(1): PP. 1-6.
  • 16. Dale, D. A. M., E. Seborg, T. F. Edgar and F. J. Doyle, Process Dynamics and Control, 2011, 3 John Wiley & Sons.
  • 17. Jalali, A. A. and V. Nadimi, A Survey on Robust Model Predictive Control from 1999-2006, International Conference on Computational Intelligence for Modeling Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, 2006, IEEE computer society.
  • 18. Di Ruscio, D., Model predictive control and identification: A linear state space model approach, Proc. Of the 36th IEEE Conference on Decision and Control, December 10-12, 1997, San Diego, USA.
  • 19. Di Ruscio, D., Model predictive control and optimization, Lecture notes for Master's course (SCE 4106), 2012, Telemark University College, Norway.
  • 20. Di Ruscio, D, Model predictive control and optimization, 2001, Telemark University College.
  • 21. Nunes, G. C, Design and Analysis of Multivariable Predictive Control Applied to an oil-water-gas seperator: A Polynomial Approach, 2001, University of Florida.
  • 22. Mohsin, M., Model Predictive control (MPC) with integral action;Reducing the control horizon and model free MPC, Lecture notes for Master's thesis, 2013, Telemark University College, Norway.
  • 23. Johansson, K. H, the Quadruple-Tank Process: A Multivariable Laboratory Process with an Adjustable Zero, IEEE Transactions on Control Systems Technology, 2000. 8: p. 456-465.
  • 24. Nagarajapandian, M., Kanthalakshmi S and Anitha T. Design and Implementation of Controllers for Quadruple Tank System, Journal of Control & Instrumentation, 2018. 9(1): pp. 25–32.
  • 25. IDivya, K. M. Nagarajapandian, and T. Anitha, Design and Implementation of Controllers for Quadrupe Tank System, International Journal of Advanced Research in Education & Technology IJARE), April – June, 2017. 4(2): pp. 158-165.
  • 26. Kirubakarana, V., T. K. Radhakrishnana and N.Sivakumaranb, Distributed multiparametric model predictive control design for a quadruple tank process, Elsevier, January 2014. 47: pp. 841-854.

Constrained model predictive control for the quadruple-tank process

Year 2019, Volume: 3 Issue: 3, 175 - 181, 15.12.2019
https://doi.org/10.35860/iarej.535552

Abstract

Model Predictive Control (MPC) is an advanced method of
controllers, explicitly uses of model to obtain control signal. MPC is popular
in industry and academia because it is capable to deals with non-minimum phase,
unstable, dead time and multivariable processes, and solves the problem of
constraints. MPC with integral action method is used in this study for the
quadruple tank system by taking the lower two tanks into account. The objective
of this work is to design and study the MPC method for controlling the level of
tanks in a quadruple tank process depending on type of constrained problems.
However, to solve the problem of constraints is not easy way. The methods based
on the quadratic programming function and ‘if-else’ technique are presented to solve
the problem of the process constraints in MPC. A comparative study is performed
with the quadratic programming function and ‘if-else’ technique. The
performance of the proposed method is tested for reference tracking and
disturbance rejection behavior. Simulation results are presented and discussed
to show the performance of the controller.

References

  • 1. Morari, M. and J. H. Lee, Model predictive control: Past, present, and future. Comp. Chem. Eng., 1999. 23: p. 667-682.
  • 2. Kavery, Lal., S. Rakesh Kumar and R. Valarmathi, Model predictive control for MIMO process, ARPN Journal of Engineering and Applied Sciences, April 2018. 13(7): pp. 2666-2670.
  • 3. Gireesh, K. J. and S. Veena, Model Predictive Controller design for performance study of coupled tank process, ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE), 2013. 1(3): pp. 70-74.
  • 4. A. Afram, A. and F. Janabi-Sharifi, Theory and applications of HVAC control systems – A review of model predictive control (MPC), Building and Environment, 2014. 72: pp. 343-355.
  • 5. Rawlings, J. B., E. S. Meadows and K. R. Muske, Nonlinear model predictive control: A tutorial and survey. In IFAC ADCHEM'94, Kyoto, Japan, 1994. p.185-197.
  • 6. Qin, S. J and T. A. Badgwell, An overview of industrial model predictive technology. In Chemical Process Control V, Tahoe City, CA, 1997. p. 232-256.
  • 7. Richalet, J., Industrial applications of model based predictive control, Automatica, 1993. 29: p. 1251-1274.
  • 8. Abu-Ayyad, M. and R. Dubay, Real-time comparison of a number of predictive controllers, ISA Transactions, 2007. 46: p. 411-418.
  • 9. Holkar, K. S. and L. M. Waghmare, Discrete Model Predictive Control for DC drive Using Orthonormal Basis function, UKACC International Conference on control, Coventry, UK, 2010. p. 435-440.
  • 10. Mohanty, S., Artificial neural network based system identification and model predictive control of a flotation column, Journal of Process Control, 2009. 19: p. 991–999.
  • 11. Camacho, E. F. and C. Bordons, Model Predictive Control in the process industry, 1995, Springer.
  • 12. Camacho, E. F., Constrained generalized predictive control, IEEE Trans. Automat. Contr. , 1993. 38: p. 327-332.
  • 13. Rossiter, J. A. and B. Kouvaritakis, Constrained stable generalized predictive control, IEE Proc., Pt.D, 140, 1993. 4: p. 243-254.
  • 14. Kothare, M. V., V. Balakrishnan and M. Morari, Robust constrained model predictive control using linear matrix inequalities, Automatica, 1996. 32(10): p.1361-1379.
  • 15. Zidane, Z., Unconstrained and Constrained Predictive Control for the Multivariable Process with Non-minimum Phase, Journal of Modeling and Simulation of Materials, 2019. 2(1): PP. 1-6.
  • 16. Dale, D. A. M., E. Seborg, T. F. Edgar and F. J. Doyle, Process Dynamics and Control, 2011, 3 John Wiley & Sons.
  • 17. Jalali, A. A. and V. Nadimi, A Survey on Robust Model Predictive Control from 1999-2006, International Conference on Computational Intelligence for Modeling Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, 2006, IEEE computer society.
  • 18. Di Ruscio, D., Model predictive control and identification: A linear state space model approach, Proc. Of the 36th IEEE Conference on Decision and Control, December 10-12, 1997, San Diego, USA.
  • 19. Di Ruscio, D., Model predictive control and optimization, Lecture notes for Master's course (SCE 4106), 2012, Telemark University College, Norway.
  • 20. Di Ruscio, D, Model predictive control and optimization, 2001, Telemark University College.
  • 21. Nunes, G. C, Design and Analysis of Multivariable Predictive Control Applied to an oil-water-gas seperator: A Polynomial Approach, 2001, University of Florida.
  • 22. Mohsin, M., Model Predictive control (MPC) with integral action;Reducing the control horizon and model free MPC, Lecture notes for Master's thesis, 2013, Telemark University College, Norway.
  • 23. Johansson, K. H, the Quadruple-Tank Process: A Multivariable Laboratory Process with an Adjustable Zero, IEEE Transactions on Control Systems Technology, 2000. 8: p. 456-465.
  • 24. Nagarajapandian, M., Kanthalakshmi S and Anitha T. Design and Implementation of Controllers for Quadruple Tank System, Journal of Control & Instrumentation, 2018. 9(1): pp. 25–32.
  • 25. IDivya, K. M. Nagarajapandian, and T. Anitha, Design and Implementation of Controllers for Quadrupe Tank System, International Journal of Advanced Research in Education & Technology IJARE), April – June, 2017. 4(2): pp. 158-165.
  • 26. Kirubakarana, V., T. K. Radhakrishnana and N.Sivakumaranb, Distributed multiparametric model predictive control design for a quadruple tank process, Elsevier, January 2014. 47: pp. 841-854.
There are 26 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Zohra Zıdane 0000-0002-5603-8011

Publication Date December 15, 2019
Submission Date March 4, 2019
Acceptance Date October 27, 2019
Published in Issue Year 2019 Volume: 3 Issue: 3

Cite

APA Zıdane, Z. (2019). Constrained model predictive control for the quadruple-tank process. International Advanced Researches and Engineering Journal, 3(3), 175-181. https://doi.org/10.35860/iarej.535552
AMA Zıdane Z. Constrained model predictive control for the quadruple-tank process. Int. Adv. Res. Eng. J. December 2019;3(3):175-181. doi:10.35860/iarej.535552
Chicago Zıdane, Zohra. “Constrained Model Predictive Control for the Quadruple-Tank Process”. International Advanced Researches and Engineering Journal 3, no. 3 (December 2019): 175-81. https://doi.org/10.35860/iarej.535552.
EndNote Zıdane Z (December 1, 2019) Constrained model predictive control for the quadruple-tank process. International Advanced Researches and Engineering Journal 3 3 175–181.
IEEE Z. Zıdane, “Constrained model predictive control for the quadruple-tank process”, Int. Adv. Res. Eng. J., vol. 3, no. 3, pp. 175–181, 2019, doi: 10.35860/iarej.535552.
ISNAD Zıdane, Zohra. “Constrained Model Predictive Control for the Quadruple-Tank Process”. International Advanced Researches and Engineering Journal 3/3 (December 2019), 175-181. https://doi.org/10.35860/iarej.535552.
JAMA Zıdane Z. Constrained model predictive control for the quadruple-tank process. Int. Adv. Res. Eng. J. 2019;3:175–181.
MLA Zıdane, Zohra. “Constrained Model Predictive Control for the Quadruple-Tank Process”. International Advanced Researches and Engineering Journal, vol. 3, no. 3, 2019, pp. 175-81, doi:10.35860/iarej.535552.
Vancouver Zıdane Z. Constrained model predictive control for the quadruple-tank process. Int. Adv. Res. Eng. J. 2019;3(3):175-81.



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