Utilizing Fuzzy an Effective Approach for Enhancing the Performance of BLDCM

Abstract

Because of its high power density and simplicity of control to reach the appropriate level of performance, the BLDC drive is widely employed in a variety of industries. For the motor to operate at its best, an appropriate speed control is needed. In permanent magnet motors, speed control is often accomplished using the traditional proportional integral (PI) controller. PI controllers are simple to use and have a straightforward control structure, however they have issues with non-linearities and load conditions and other complex situations. Yet, an exact linear mathematical model is necessary for PI controllers. In this work, a fuzzy is suggested as a way to alleviate the torque ripple in Motor drives. To limit ripple and enhance the dynamic response of drive system, fuzzy logic is used. In comparison to the PI control, the FLC enhances the speed response quality and lessens torque output ripple.

Country : India

1 Shahuraj S. Sable2 Dr. Ashok Kumar Jhala

  1. Bhabha University, Bhopal, Madhya Pradesh, India
  2. Bhabha University, Bhopal, Madhya Pradesh, India

IRJIET, Volume 7, Special Issue of ICRTET- 2023 pp. 19-21

IRJIET.ICRTET05

References

  1. J. Liu, and Y. Guo, "A Novel Fuzzy Control Strategy for Ripple Reduction in BLDC Motor," IEEE Access, vol. 8, pp. 145355- 145365, 2020.
  2. C. Wang, and H. Wang, "Fuzzy logic control of BLDC motor based on variable structure observer for ripple reduction," International Journal of Electrical Power and Energy Systems, vol. 128, pp. 106- 113, 2021.
  3. S. Wu, and Q. Li, "Ripple Reduction Control of BLDCM Based on Fuzzy Logic and Variable Structure Control," IEEE Transactions on Power Electronics, vol. 35, no. 2, pp. 2042-2050, 2020.
  4. R. Zhang, Y. Chen, "Fuzzy logic-based current control for ripple reduction in BLDC motor using sliding mode observer," Electric Power Systems Research, vol. 184, p. 106269, 2020.
  5. S. Jain, and S. K. Jain, "Ripple Current Reduction in BLDC Motor Using Fuzzy Controller," in Proceedings of the 2021 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE), 2021, pp. 1-6.
  6. Ramirez J, Salas-Gonzalez D, Alvarez I. NMFSVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Transactions on medical imaging. 2019 Sep 12;31(2):207-16.
  7. Lu H, Pan Z. Automated diagnosis of Alzheimer’s disease using Gaussian mixture model based on cortical thickness.2019 Oct 18 (pp. 880-883). IEEE.