Improved torque performance in BLDC-motor-drive through Jaya optimization implemented on Xilinx platform
Robust dynamic speed response with less maintenance and efficient operation makes the Brushless Direct Current (BLDC) motors as an oblivious choice for many motoring applications which needs instantaneous speed control while developing high torque. This paper proposes Jaya optimization Algorithm on...
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Published in | Microprocessors and microsystems Vol. 81; p. 103681 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Kidlington
Elsevier B.V
01.03.2021
Elsevier BV |
Subjects | |
Online Access | Get full text |
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Summary: | Robust dynamic speed response with less maintenance and efficient operation makes the Brushless Direct Current (BLDC) motors as an oblivious choice for many motoring applications which needs instantaneous speed control while developing high torque. This paper proposes Jaya optimization Algorithm on Xilinx platform for BLDC Motor and Fractional Order Proportional Integral Derivative controller to improve its efficiency through minimizing the ripples present in the Torque of the motor. The control strategy of torque controller works with the combinations of Xilinx and optimization algorithms. In this proposed controller, the strategy of control is achieved after deriving the BLDC motor's dynamic mode there after the electronic interaction with BLDC motor is enrooted with the Math lab/Simulink model while tactically utilizing Xilinx tools for all kind of co-simulation in the same model. Initially the dynamic model of a BLDC motor drive is derived and the control topology is designed with the help of the proposed controller. The controlling mechanism established in this paper is effective and efficient in minimizing the ripples present in the torque of the motor. The proposed controller is utilized for possible low-cost and high-performance industrial applications. The results are obtained and analyzed with existing methodologies and it shows that the proposed technique outperforms the existing models. |
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ISSN: | 0141-9331 1872-9436 |
DOI: | 10.1016/j.micpro.2020.103681 |