Journal
APPLIED SCIENCES-BASEL
Volume 12, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/app122110749
Keywords
sliding mode control; support vector regression; machine learning; PMSM; super-twisting algorithm
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Funding
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [NRF-2019R1A2C1002343]
- BK21 FOUR Project
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In this paper, sliding mode control and disturbance compensation techniques are applied to a nonlinear speed control algorithm for a permanent magnet synchronous motor (PMSM). By utilizing the super-twisting algorithm reaching law (STRL) for sliding mode control and support vector regression-disturbance observer (SVR-DOB) for disturbance compensation, the control performance of PMSM systems is optimized, resulting in improved precision and disturbance rejection.
Sliding mode control and disturbance compensation techniques are applied to a nonlinear speed control algorithm for a permanent magnet synchronous motor (PMSM). Optimizing the speed control performance of PMSM systems with various disturbances and uncertainties is challenging. To achieve a satisfactory performance, a sliding mode control method based on the super-twisting algorithm reaching law (STRL) is presented. STRL can adapt dynamically to the variations of a controlled system. The STRL maintains a high tracking performance of the controller and allows the control input to eliminate chattering. To estimate the uncertainties and compensate for disturbances, a support vector regression-disturbance observer (SVR-DOB) is presented. The estimated uncertainties were used to minimize modeling errors and improve the disturbance rejection. A controller using SVR-DOB achieves a high precision, and the simulation results demonstrated the validity of the proposed control approach.
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