Unconstrained and Constrained Predictive Control for the Multivariable Process with Non-minimum Phase

  • Zohra Zidane Team of Applied Physics and New Technologies, Department of Physic, Polydisciplinary Faculty, University of Sultan Moulay Slimane, B.P: 592, 23000 Beni-Mellal,

Abstract

Non-minimum phase Multi-input Multi-Ouput (MIMO) systems are known to be difficult to control. Model Predictive Control (MPC) algorithms are powerful control design methods widely applied to industrial processes. The handling of various input constraints in the MPC problem of ARIMAX non-minimum phase MIMO systems is considered. This approach is applied for control of industrial quadruple tanks. However, there is no easy way to solve the problem of constraints. The methods based on the quadratic programming (QP) technique are used to solve the constrained optimization problem. A comparative study of unconstrained and constrained control system behavior is given. Some illustrative simulation results for a considered system are presented and discussed. Encouraging results are obtained that motivate for further investigations.

Keywords: ARIMAX systems, Model Predictive Control, MIMO systems, Non-minimum Phase systems.

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References


  1. Kavery, S. Rakesh Kumar, and R. Valarmathi, “Model predictive control for MIMO process”, ARPN Journal of Engineering and Applied Sciences, vol. 13, No. 7, pp. 2666-2670, April 2018. View Article

  2. T. C. Tsang, and D. W. Clarke, “Generalized predictive control with input constraints”, In IEE Proceedings D (Control Theory and Applications) IET Digital Library, Vol. 135, No. 6, pp. 451-460, Nov. 1988. View Article

  3. E. Orukpe, “Model predictive control fundamentals”,Nigerian Journal of Technology, Vol. 31, No. 2, pp. 139-148, July, 2012. View Article

  4. Di Ruscio, “Model predictive control and identification: A linear state space model approach”, Proc. Of the 36th IEEE Conference on Decision and Control, Vol. 4, pp. 3202-3209, December 10-12, San Diego, USA, 1997. View Article

  5. F. Camacho, “Constrained generalized predictive control”,IEEE transactions on automatic control, Vol. 38, No. 2, pp. 327-332, 1993. View Article

  6. Wang, “Model Predictive Control System Design and Implementation Using MATLAB”, MATLAB®. Springer Science & Business Media, 2009. View Article

  7. A. Rossiter, and B. Kouvaritakis “Constrained stable generalized predictive control”, IEE proceedings d-control theory and applications, Vol.140, No.4, pp. 243-254, 1993. View Article

  8. V. Kothare, V. Balakrishnan, and M. Morari, “Robust constrained model predictive control using linear matrix inequalities”, Automatica, Vol. 32, No.10, pp.1361-1379, 1996. View Article

  9. Afram, and F. Janabi-Sharifi, “Theory and applications of HVAC control systems – A review of model predictive control (MPC)”, Building and Environment, Vol. 72, pp. 343-355, 2014. View Article

  10. H. Johansson, “The quadruple-tank process: A multivariable laboratory process with an adjustable zero”, IEEE Transactions on control systems technology, Vol. 8, No. 3, pp. 456-465, 2000. View Article

  11. K .IDivya, M. Nagarajapandian, and T. Anitha, “Design and Implementation of Controllers for Quadrupe Tank System”, International Journal of Advanced Research in Education & Technology (IJARET), Vol. 4, No. 2, April – June, 2017. View Article

Published
2019-07-11
How to Cite
[1]
Z. Zidane, “Unconstrained and Constrained Predictive Control for the Multivariable Process with Non-minimum Phase”, J. Mod. Sim. Mater., vol. 2, no. 1, pp. 1-6, Jul. 2019.
Section
Research Article