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Software model of an intelligent control system for complex processes of small-scale ore processing

Intelligent process control systems allow analyzing incoming information about the control object and the external environment at a qualitatively new level and, on this basis, increasing the efficiency of the entire production. The article presents the results of a study aimed at developing a software model of an intelligent control system for complex processes of small-scale ore processing. The complexity of the processes lies in the interrelationship of many variables describing the technological units involved in processing. An analytical description of such interrelations allows describing processes with high accuracy, but leads to complex calculations that are difficult to apply in real conditions. The use of new computational algorithms from the group of intelligent methods made it possible to overcome this contradiction, ensuring, on the one hand, good accuracy of solutions, and on the other hand, making it possible to automate the adjustment of the control system to changing input effects and external conditions. The basis of the proposed software model is a deep neural network of long short-term memory, solving the regression problem when analyzing input data and calculating control effects. The novelty of the research results is the structure of the software model of the intelligent control system, including a neural network as a regulator and a hierarchical system of fuzzy inference for a generalized assessment of the control qualities. An original feature of the software structure is the use of a calculator of derivatives of different orders to feed them to the input of the neural network, which helps to expand its receptive field and increases the accuracy of its results. Testing of the proposed structure of the software model was carried out in the MatLab-Simulink environment. The results of the simulation experiment showed that, unlike the PID controller, the use of a deep neural network as a regulator allows for successful compensation of the influence of external factors on the quality of control. Read more...