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.
Highlights
The heating and ventilation system plays an important role in our daily life where certain desired temperature is controlled in order to maintain the healthy and safe working environment to the conditioned space
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
Summary
The heating and ventilation system plays an important role in our daily life where certain desired temperature is controlled in order to maintain the healthy and safe working environment to the conditioned space. The State Space Model Predictive Control (SSMPC) with actuators constraints is considered for a pilot scale aerothermic process. To fulfil the requirement for integral action in most industrial control systems, we have embedded the SSMPC design model with integrators to achieve this objective and ensure outputs steadystate error free This strategy is transformed into a Quadratic Programming (QP) problem, in which a quadratic cost function subject to linear inequality constraints is minimized on-line. The objectives of the proposed control technique which are about reaching reference set-points for the temperature and the air flow, subject to effects of both actuators constraints and the external perturbations These goals are achieved by manoeuvring the heating resistance and the ventilator speed under constraints on the manipulated variables and their rate of change to handle the factory set thermal protection. Robustness of the SSMPC controller is discussed and a final conclusion is given
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