SavedAdd your journal

Journals / International Advanced Researches and Engineering Journal / 2019 / Cilt: 3 - Sayı: 3

Constrained model predictive control for the quadruple-tank process

Zohra ZİDANE
Pages
175–181
Publication Date
2022-10-19
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Simulation results are presented and discussed to show the performance of the controller.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Model Predictive Control (MPC) is an advanced method of controllers, explicitly uses of model to obtain control signal.
📊
Key Findings & Data:The performance of the proposed method is tested for reference tracking and disturbance rejection behavior.
💡
Conclusion & Impact:Simulation results are presented and discussed to show the performance of the controller.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Model predictive control · MIMO systems · ARIMAX systems

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.

🔖

Cite This Article

ZİDANE, Z. (2022). Constrained model predictive control for the quadruple-tank process. International Advanced Researches and Engineering Journal, Cilt: 3 - Sayı: 3, 175–181. https://ajindex.com/articles/539357

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  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 P