Rotor Position Self-Sensing of SRM Using PSO-RVM
AbstractThe motors’ flux-linkage, current and angle obtained from the system with sensors were chosen as the sample data, and the estimation model of rotor position based on relevance vector machine (RVM) was built by training the sample data. The kernel function parameter in RVM model was optimized by the particle swarm algorithm in order to increase the fitting precision and generalization ability of RVM model. It achieved higher prediction accuracy with staying at the same on-line testing time as the RVM. And because the short on-line computation, the motor can operate at 3000 r/min in sensorless control with particle swarm optimization-relevance vector machine (PSO-RVM), which is higher than support vector machine (SVM) and neural network (NN). By simulation and experiment on the test motor, it is verified that the proposed estimation model can obtain the angle of full electrical period accurately under low speed and high speed operations in current chopped control and angle position control, which has satisfactory estimation precision. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Xiang, Q.; Yuan, Y.; Yu, Y.; Chen, K. Rotor Position Self-Sensing of SRM Using PSO-RVM. Energies 2018, 11, 66.
Xiang Q, Yuan Y, Yu Y, Chen K. Rotor Position Self-Sensing of SRM Using PSO-RVM. Energies. 2018; 11(1):66.Chicago/Turabian Style
Xiang, Qianwen; Yuan, Ye; Yu, Yanjun; Chen, Kunhua. 2018. "Rotor Position Self-Sensing of SRM Using PSO-RVM." Energies 11, no. 1: 66.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.