Parameter-Free Model Predictive Current Control for PMSM Based on Current Variation Estimation without Position Sensor

Laiwu Luo, Feng Yu (), Lei Ren and Cheng Lu
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Laiwu Luo: School of Electrical Engineering, Nantong University, Nantong 226019, China
Feng Yu: School of Electrical Engineering, Nantong University, Nantong 226019, China
Lei Ren: School of Electrical Engineering, Nantong University, Nantong 226019, China
Cheng Lu: School of Electrical Engineering, Nantong University, Nantong 226019, China

Energies, 2023, vol. 16, issue 19, 1-14

Abstract: To remove parameter dependence in existing sensorless control strategies, a parameter-free model predictive current control is proposed for permanent magnet synchronous motor without any position sensor. First, the current variation during one sampling period is analyzed and divided into two elements: natural attenuation and forced response. Second, recursive least squares algorithm is utilized to estimate the future current variation so that the model predictive current control can be successfully executed paying no attention to motor parameters. Meanwhile, the position information is obtained by the arc tangent function according to the estimated forced response of current variation. At last, experimental results verify that the estimation errors of rotor position are reduced to around 0.1 rad with smaller current prediction error even at low speed where no motor parameters are required.

Keywords: sensorless control; parameter-free; model predictive current control; permanent magnet synchronous motor; recursive least squares algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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