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Artificial Neural Network Motor Control for Full-Electric Injection Moulding Machine

  • This paper proposes a new artificial neural network-based position controller for a full-electric injection moulding machine. Such a controller improves the dynamic characteristics of the positioning for hot runners, pin valve and the injection motors for varying moulding parameters. Practical experimental data and Matlab’s System Identification Toolbox have been used to identify the transfer functions of the motors. The structure of the artificial neural network, which used positioning error and speed of error, was obtained by numerical modelling in Matlab/Simulink. The artificial neural network was trained using back-propagation algorithms to provide control of the motor current thus ensuring the required position and velocity. The efficiency of the proposed ANN-based controller has been estimated and verified in Simulink using real velocity data and the position of the injection moulding machine and pin valve motors.

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Document Type:Conference Object
Author:Oleksandr Veligorskyi, Roustiam Chakirov, Maksym Khomenko, Yuriy Vagapov
Parent Title (English):2019 IEEE International Conference on Industrial Technology (ICIT), Melbourne, Australia, 13-15 Feb. 2019
First Page:60
Last Page:65
Date of first publication:2019/07/04
Tag:artificial neural networks; brushless motors; control; injection moulding; motor drive; permanent magnet motors
Departments, institutes and facilities:Fachbereich Elektrotechnik, Maschinenbau, Technikjournalismus
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Entry in this database:2019/07/13