Sensorless current model control for permanent magnet synchronous motor based on IPID with two-dimensional cloud model online optimisation

• Published in: IET power electronics Vol. 12; no. 5; pp. 983 - 993
• Main Authors: Wei, Haifeng, Tao, Heng, Duan, Fuqiao, Zhang, Yi, Li, Yuanjiang, Luo, Zhihong
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.05.2019
• Subjects: chaos particle swarm optimisation algorithm; intelligent proportion integration differentiation; IPID; motor internal parameter perturbation; online adjustment module; online optimisation; parameter dynamic real‐time self‐tuning function; particle swarm optimisation; permanent magnet motors; permanent magnet synchronous motor; position control; real‐time rotor position; Research Article; rotors; sensorless current model control algorithm; sensorless machine control; speed estimation; synchronous motors; three‐term control; two‐dimensional cloud model; virtual γ−δ rotating coordinate system; IPID; three-term control; rotors; real-time rotor position; particle swarm optimisation; sensorless machine control; chaos particle swarm optimisation algorithm; speed estimation; synchronous motors; two-dimensional cloud model; motor internal parameter perturbation; virtual γ−δ rotating coordinate system; parameter dynamic real-time self-tuning function; sensorless current model control algorithm; permanent magnet synchronous motor; intelligent proportion integration differentiation; position control; online adjustment module; permanent magnet motors; online optimisation
• ISSN: 1755-4535; 1755-4543
• DOI: 10.1049/iet-pel.2018.5892

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 $\gamma - \delta $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.