Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Generalized predictive control—Part II. Extensions and interpretations
Automatica (Journal of IFAC)
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry
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This study focuses firstly on the numerical investigation of an air conditioning unit prototype developed to guarantee a microclimate with controlled temperature and relative humidity set points for crop growth chambers. Numerical techniques based on Computational Fluid Dynamics (CFD) were implemented to analyze the flow characteristics of the mixed air produced by the device. Simulations disclose a non-linear relationship between the air flow rate and the aperture opening, and also show that the mixing zone was not perfect but could be improved by the addition of baffles. The promising results obtained from CFD were used to improve the automation of the device for potential application in closed greenhouses. Secondly, the indirect adaptive generalized predictive control strategy (IAGPC) with a decentralized architecture was used to control the temperature of the air conditioning unit. As the process involves time-varying, the use of a recursive estimation approach with a fixed forget factor was adopted to estimate in real time the system parameters, and to adapt simultaneously the GPC controller parameters. In order to achieve a local and global efficient performance, the proposed decentralized IAGPC architecture was applied to two principal subsystems, which compose the global unit. A significant real time experimental improvement in the system performance is observed on temperature control for a wide range of operating points.