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D. M. de la Pena, D. R. Ramirez, E. F. Camacho and T. Alamo, “Application of an Explicit Min-Max MPC to a Scaled Laboratory Process,” Control Engineering Practice, Vol. 13, No. 12, 2005, pp. 1463-1471. doi:10.1016/j.conengprac.2004.12.008

has been cited by the following article:

• TITLE: State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints

  AUTHORS: Mustapha Ramzi, Hussein Youlal, Mohamed Haloua

  KEYWORDS: Multi-Variable Control; Aerothermic Process; Actuators Constraints; Process Identification; State Space Model Predictive Control

  JOURNAL NAME: Intelligent Control and Automation, Vol.3 No.1, February 28, 2012

  ABSTRACT: This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.