永磁同步电机的无差拍电流预测控制优化算法

范佩樟; 刘凌; 靳东松; 刘思源

doi:10.7652/xjtuxb202304004

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永磁同步电机的无差拍电流预测控制优化算法

—————————————————————— | 更新时间：2025-07-09

- 永磁同步电机的无差拍电流预测控制优化算法
- Optimization Algorithm of Deadbeat Current Predictive Control for Permanent Magnet Synchronous Motor
- 西安交通大学学报 2023年57卷第4期页码：29-38
- 作者机构：
  
  1. 西安交通大学电力设备电气绝缘国家重点实验室,西安,710049
  2. 西安交通大学电气工程学院,西安,710049
- 作者简介：
  
  : 2022-11-23。作者简介: 范佩樟(1997—),男,硕士生
  刘凌(通信作者),男,副教授,博士生导师。基金项目: 国家自然科学基金资助项目(51977173)。
- 基金信息：
  
  : 2022-11-23。作者简介: 范佩樟(1997—),男,硕士生;刘凌(通信作者),男,副教授,博士生导师。基金项目: 国家自然科学基金资助项目(51977173)。
- DOI：10.7652/xjtuxb202304004
  中图分类号： TM301
- 网络首发：2023-04-10，
  
  纸质出版：2023
- 稿件说明：
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范佩樟, 刘凌, 靳东松, 等. 永磁同步电机的无差拍电流预测控制优化算法[J]. 西安交通大学学报, 2023,57(4):29-38.

FAN Peizhang, LIU Ling, JIN Dongsong, et al. Optimization Algorithm of Deadbeat Current Predictive Control for Permanent Magnet Synchronous Motor[J]. 2023, 57(4): 29-38.
范佩樟, 刘凌, 靳东松, 等. 永磁同步电机的无差拍电流预测控制优化算法[J]. 西安交通大学学报, 2023,57(4):29-38. DOI： 10.7652/xjtuxb202304004.

FAN Peizhang, LIU Ling, JIN Dongsong, et al. Optimization Algorithm of Deadbeat Current Predictive Control for Permanent Magnet Synchronous Motor[J]. 2023, 57(4): 29-38. DOI： 10.7652/xjtuxb202304004.

摘要

针对表贴式永磁同步电机(SPMSM)无差拍电流预测控制在电机参数发生偏差时会导致系统失稳

d、q轴电流发生大幅度振荡的问题

提出了一种鲁棒前馈电流预测控制算法。用前一时刻的参考电流和当前时刻的采样电流代替传统电压预测式中的当前时刻的采样电流

改变采样电流与参考电流的闭环传递函数

采用鲁棒因子对系统的稳定裕度进行调节

提高了控制系统的鲁棒性和稳定裕度。仿真结果表明

当模型电感分别是参考电感的2倍和4倍时

所提出的鲁棒前馈电流预测控制算法比传统算法q轴电流的方差分别减少了61.5%和67.3%。此外

针对电机参数失配时稳态电流静差扩大的问题

提出了一种增量式电流静差消除算法。通过相邻时刻预测电压、实际电流与参考电流的差值来计算下一时刻的给定电压

消除电机磁链参数对稳态电流静差的影响; 利用实际电流与参考电流的误差对给定电压进行补偿

以减少稳态电流静差。仿真结果表明

当模型磁链分别是参考磁链的0.5倍、0.4倍和0.375倍时

所提增量式电流静差消除算法比传统算法q轴电流的误差分别减少了38.69%、56.94%和54.66%。

Abstract

A robust feed forward current predictive control algorithm was proposed to solve the problem that the deadbeat current predictive control of surface mounted permanent magnet synchronous motor(PMSM)causes the system instability and the d

q axis current oscillates greatly due to the motor parameter deviation. Firstly

the reference current at the previous time and the sampling current at the current time were used to replace the sampling current at the current time in the traditional voltage prediction formula to change the close-loop transfer function of the sampling current and reference current

and then the robust factor was used to adjust the stability margin of the system to improve the robustness and stability margin of the control system. The simulation results showed that when the model inductance was 2 and 4 times of the reference inductance respectively

the variance of the proposed robust feedforward current predictive control algorithm was reduced by 61.5% and 67.3% compared with the traditional algorithm. In addition

to solve the problem that the steady state current static error expands when the motor parameters are mismatched

an incremental current static error elimination algorithm was proposed. Firstly

the given voltage at the next time was calculated by the difference between the predicted voltage

actual current and reference current at the adjacent time to eliminate the influence of motor flux parameters on the static error of steady state current. Then

the error between actual current and reference current was used to compensate the given voltage to reduce the static error of steady state current. The simulation results showed that when the flux linkage of the model was 0.5 times

0.4 times and 0.375 times of the reference flux linkage

respectively. The error of the proposed incremental current static error elimination algorithm was reduced by 38.69%

56.94% and 54.66% compared with the traditional algorithm for q-axis current.

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references

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