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Article title ON DEVELOPMENT OF NEURAL NETWORK TUNER FOR CONTOUR OF VELOCITY IN ROLLING MILL ELECTRIC DRIVE FOR REDUCING DYNAMIC LOADS
Authors Yu. I. Eremenko, A. I. Gluschenko, V. A. Petrov
Section SECTION II. DESIGNING MANAGEMENT INFORMATION AND AUTOMATED SYSTEMS
Month, Year 09, 2017 @en
Index UDC 004.89:681.51
DOI
Abstract A problem of dynamic load reduction influencing a rolling mill is considered. The dynamic load occurring in function-generating mechanisms results in technical equipment excessive wear and even its breakdown. A short analysis of main methods for solving this problem is made. The dynamic load is to be reduced by the means of an automatic control system. In particular, a neural network tuner for the velocity contour controller in rolling mill electric drive is proposed to achieve that aim. The tuner consists of a neural network and a rule base determining moments of learning the network and the learning rate value to be used to do that. The rule base operational algorithm is proposed to reject disturbances caused by the roll bite and reduce dynamic force at moment of such disturbances occurrence. Having found that moment, the tuner adjusts the PI-controller velocity parameters of the drive in accordance with that algorithm. The main advantage of such approach is that there is no necessity to identify the control object. In this research the model of the second mill in the train of 350 Oskol eletrometallurgical plant is used. The tuner is applied to this mathematical model with multi-mass mechanical part implemented in MATLAB & Simulink and a test bench with a DC drive. Having analyzed the experiments results, the conclusion could be made that the tuner allows to: 1) reduce dynamic inertia moment oscillations by 18% in comparison with the control system without the tuner, 2) improve energy efficiency of the disturbance rejection by 3.8 %.

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Keywords Dynamic force; rolling mill; elastic coupling; electric drive; PI-controller; neural tuner.
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