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Energies 2018, 11(1), 66;

Rotor Position Self-Sensing of SRM Using PSO-RVM

School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Author to whom correspondence should be addressed.
Received: 31 October 2017 / Revised: 11 December 2017 / Accepted: 20 December 2017 / Published: 1 January 2018
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The 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
Keywords: relevance vector machine (RVM); particle swarm; switched reluctance motor; estimation model; self-sensing relevance vector machine (RVM); particle swarm; switched reluctance motor; estimation model; self-sensing

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Xiang, Q.; Yuan, Y.; Yu, Y.; Chen, K. Rotor Position Self-Sensing of SRM Using PSO-RVM. Energies 2018, 11, 66.

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