Journal of System Simulation
Abstract
Abstract: In order to solve the poor accuracy of the speed and rotor position of permanent magnet synchronous motor caused by sensor, a sensorless control system is proposed to calculate the speed and rotor position of PMSM with extended Kalman filtering algorithm. BP neural network algorithm is used to optimize the covariance matrix Q and R of EKF, which improves the accurate calculation values of rotational speed and rotor position. At the same time, the speed sliding mode controller combined with the current feed-forward decoupling unit are used to improve the stability of the whole control system. The simulation results show that the system can accurately calculate speed and rotor position and the deviation value of rotor position fluctuates around ±0.3 rad. Compared with the traditional PI control, the speed recovery time is shortened by 50%, and the overshoot is very small, the robustness is stronger. It has strong practical application value in motor control.
Recommended Citation
Ma, Lixin; Zhu, Yongjie; and Ji, Leyan
(2021)
"Neural Network Optimized Sensorless Permanent Magnet Synchronous Motor Control System,"
Journal of System Simulation: Vol. 33:
Iss.
3, Article 12.
DOI: 10.16182/j.issn1004731x.joss.19-0562
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss3/12
First Page
622
Revised Date
2019-12-06
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-0562
Last Page
630
CLC
TM383.4;TP391
Recommended Citation
Ma Lixin, Zhu Yongjie, Ji Leyan. Neural Network Optimized Sensorless Permanent Magnet Synchronous Motor Control System[J]. Journal of System Simulation, 2021, 33(3): 622-630.
DOI
10.16182/j.issn1004731x.joss.19-0562
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