This paper presents the application of two model-based predictive control (MPC) algorithms on the cooling system of an office building. The two strategies discussed are a simple MPC, and an adaptive MPC algorithm connected to a model predictor. The cooling method used represents the air-conditioning unit of an HVAC system. The temperature of the building’s three rooms is controlled with fan coil units, based on the reference temperature and with different constraints applied. Furthermore, the building model is affected by dynamically changing interior and exterior heat sources, which we introduced into the controller as disturbances.
Hybrid systems contain both continuous dynamics and discrete elements, where discrete transitions happen between different continuous operating modes as a consequence of certain events. The notion of modeling these types of systems is discussed in regard to the four tank process, which has hybrid and nonlinear characteristics. This paper describes the modeling of this system in Simscape software and a hybrid model is created as well, with a hybrid controller being built around it that includes finite-state automata and PI controller. Simulations implemented in Matlab Simulink software prove the viability and correctitude of the method presented.
In this work, an explicit Model Predictive Control algorithm is devised and compared to classical control algorithms applied to a series resonant DC/DC converter circuit. In the first part, a model of the converter as a hybrid system is created and studied. In the second part, the predictive algorithm is applied and tested on the model. Finally, the designed control algorithm is compared to classical PI and sliding mode controllers.
An HVAC system contains heating, ventilation and air conditioning equipment used in office or industrial buildings. The goal of this research is to design a controller for the process of cooling an office building that is made up of three rooms. The desired room temperature can be achieved by controlling the fans making up the fan coil units and the cooling medium’s temperature. By these means the building connected to the electrical grid becomes a smart office. The used building model includes several dynamically changing interior and exterior heat sources affecting the inner climate, which introduces a level of uncertain prediction into the system. We have determined the controller’s performance by the rate of deviation from the expected temperature, the consumed electrical energy and the generated noise. The controller was created in Matlab Simulink with the possibility of migration to a Siemens PLC.
Knowledge of the surface emissivity of metals is becoming more and more important both from the material science, process modelling and control point of view. Previous research results have shown that the emissivity of most metals depends on the temperature of the surface. It has also been reported that the most important temperature region is between 300 – 1000 K degrees, where the change of the emissivity is the most intense, which is also the most significant from a process control point of view . We also report temperature dependent emissivity observed during plasma nitriding of low alloy steels . Related to one of our present research topics the study of the low alloy aluminum (AlMg1, AlMg3) emissivity has prooven relevant. In this article the developed emissivity estimation model is presented. In the first part a literature overview and the theoretical approach of the new method is discussed, followed by the experimental results for low alloy aluminium emissivity determination and a comparison with the results available in the literature.