Deep-Learning-Based Control of Sensorless Drives for Robotics and Automation
DOI:
https://doi.org/10.23918/eajse.v10i3p7Keywords:
Drive Systems, Robotics and Automation, PID Controller, Artificial Intelligence, ANFIS-based Controller, Deep Learning-based ControllerAbstract
Permanent magnet synchronous machines (PMSMs) are essential components in automation and robotics systems due to their outstanding characteristics of small size, high efficiency and low maintenance requirements. Controlling these machines requires accurate detection of the rotor position to switch the electronic components in the inverter. The use of conventional and adaptive controllers, based on the mathematical model of the drive system, does not meet the design requirements in many robotics and automation applications because the controller parameters are affected by changes in the system dynamics. This paper deals with the design of a deep learning based controller that does not rely on a mathematical model of the drive system implemented by MATLAB/Simulink. In order to evaluate the performance of the proposed intelligent controller, the performance of the drive system was compared with other controllers, including PID traditional controllers such as PID and others based on the adaptive neuro-fuzzy inference system (ANFIS). The results of the comparison and analysis of controllers for sensorless drive systems were encouraging, as the deep learning-based controller outperformed both conventional and ANFIS-based controllers. These results indicate that the deep learning-based controller has no overshoot, with minimum rise and settling time compared to other controllers.
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Data Availability Statement
All data used in this paper are available and were obtained from modeling and simulation of the driving system.
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Copyright (c) 2025 Kasim Mousa Al-Aubidy, Mrs. Izziyyah M. Alsudi, Mr. Mohammad R. Abulaila
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