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,




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


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.


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How to Cite

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.



Research Article