Sensorless current model control for permanent magnet synchronous motor based on IPID with two‐dimensional cloud model online optimisation. Issue 5 (1st May 2019)

• Record Type: Journal Article
• Authors: Wei, Haifeng; Tao, Heng; Duan, Fuqiao; Zhang, Yi; Li, Yuanjiang; Luo, Zhihong
• Is Part Of: IET power electronics. Volume 12:Issue 5(2019)
• Page Start: 983
• Page End: 993
• Publication Date: 2019-05-01
• Subjects: particle swarm optimisation -- rotors -- permanent magnet motors -- three‐term control -- synchronous motors -- sensorless machine control -- position control; permanent magnet synchronous motor -- IPID -- two‐dimensional cloud model -- sensorless current model control algorithm -- intelligent proportion integration differentiation -- speed estimation -- chaos particle swarm optimisation algorithm -- real‐time rotor position -- motor internal parameter perturbation -- online optimisation -- virtual γ−δ rotating coordinate system -- parameter dynamic real‐time self‐tuning function -- online adjustment module; Power electronics -- Periodicals; 621.31705
• DOI: 10.1049/iet-pel.2018.5892
• Languages: English
• ISSNs: 1755-4535

Abstract

Abstract : In this study, a sensorless current model control algorithm for permanent magnet synchronous motor (PMSM) is investigated based on intelligent proportion integration differentiation (IPID) with two‐dimensional cloud model online optimisation. To obtain a relatively accurate rotor position and speed estimation, in virtual γ − δ rotating coordinate system, estimation of the position and speed is performed using a current error between an actual current and the model current obtained from a current model of the motor. Simultaneously, a type of IPID with parameter dynamic real‐time self‐tuning function is designed to adjust the speed and current loop in the current model control. By introducing the chaos particle swarm optimisation (CPSO) algorithm to optimise the control parameters offline, the entire control system is in the optimised state, then the two‐dimensional cloud model online adjustment module is used to perform real‐time tuning. The experimental results show that the new control algorithm proposed can accurately estimate the real‐time rotor position and speed, which effectively avoids the influence of back EMF harmonics, and it has the robustness to motor internal parameter perturbation and external load disturbance.