Abstract
In order to optimize the quality of transition processes on a heating object of control, it is proposed to apply a neural-network tuner, which changes parameters of the PI-controller in real time. The aim of present study is to determine effectiveness of application of the tuner using a model of the heating furnace, containing a gas supply control circuit and a controlling element of this circuit. Simulation was performed on the model of a gas furnace obtained through recalculation of thermal power from the model of an electric furnace. The study confirms the capability of the proposed adaptive system to effectively execute adaptation of parameters of the controller in the presence of a controlling mechanism whose dynamics may negatively affect the quality of control.The result of applying the tuner is a decrease in the time of transition process by 25.8 % and a reduction in the total controlling influence by 22.85 %. The presence of the controlling element in this case had no significant effect on the work of a neural-network tuner. The result of research makes it possible to extend the class of objects for which a neural-network tuner can be applied. Previously, the tuner demonstrated its effectiveness only for electric furnaces where influence of the controlling element is minimal. Result of the present study makes it possible to scale up the solution for gas thermal furnaces despite a markedly greater influence of the controlling element
Highlights
Literature review and problem statementThe present stage of development of automated systems and control devices is characterized by active modernization of tools for automation and measurements
We devised a model of the gas heating furnace using the recalculation of thermal power of the electric furnace
Its special feature is the presence of a gas supply control circuit, which makes it possible to simulate behavior of actual industrial gas furnaces
Summary
The present stage of development of automated systems and control devices is characterized by active modernization of tools for automation and measurements. The choice of the PI-controller is predetermined by its wide application at thermal objects of control This solution will make it possible, by using ES, to account for the specificity of control object (such as the impossibility of forced furnace cooling), while applying an apparatus of NN would allow the system to learn during operation. Such an intelligent system is a neural network tuner of PI-controllers’ parameters [14]. That is why the purpose of present study is the operability testing of a neural-network tuner on the model of a heating furnace, taking into consideration influence of the controlling element
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