Abstract
To study the speed estimation of permanent magnet synchronous motor (PMSM) sensorless vector control system, model reference adaptive system (MARS) and radial basis function neural network (RBFN) were adopted. The method was the organic integration of the RBFN and MRAS, selecting the PMSM as the reference model, while selecting current model of PMSM as an adjustable model. Only using the difference of the q-axis estimated current and q-axis actual current as the error signal, the error signal was transported to the regulation of RBFN to gain the estimated speed. With comparative study of simulation between this novel sensorless scheme and the reference scheme, the results show that this method presented in this paper is capable of precise estimating the rotor speed under the condition of high or low speed and achieve better static and dynamic performances.
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Gao, W., Hua, X., Guo, Z. (2012). Research on Speed Estimation of PMSM Based on Radial Basis Function and MARS. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_102
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DOI: https://doi.org/10.1007/978-3-642-34041-3_102
Publisher Name: Springer, Berlin, Heidelberg
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