基于时间序列的永磁同步电机连续控制集无模型预测电流控制

doi:10.19595/j.cnki.1000-6753.tces.230159

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摘要

时间序列数据驱动模型通过采样输入输出数据将被控对象在线拟合为离散传递函数,但在连续控制集（CCS）预测控制中直接应用存在困难。为了解决这个问题,本文结合最小二乘法,提出一种基于时间序列的永磁同步电机（PMSM）连续控制集无模型预测电流控制方法。该方法通过拉格朗日法合理设计回归矢量,在线估算模型待定系数,并建立数据驱动模型预测所需变量。该方法不仅从根本上消除模型预测控制（MPC）中先验模型对被控对象时变物理参数的依赖,而且所得模型符合电机运动特性,有更高模型精度和良好控制性能。仿真和实验结果验证了提出方法的有效性,以及在动态性能、电流质量和系统噪声方面的优势。

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关键词 ：无模型预测控制, 时间序列模型, 最小二乘估计算法, 电流预测

Abstract：

The finite-control-set type (FCS-type) predictive control method is not ideal for electric vehicles because its limited control accuracy and high harmonic content. In contrast, the continuous-control-set type (CCS-type) model-free predictive current control (MF-PCC) is more suitable. The model-free predictive current control (MF-PCC) using time-series model describes the motor as a discrete transfer function, which is more compliant with the motion characteristics of the motor system. However, the difficulty of calculating the inverse of the model in the digital processor makes it challenging to apply in the CCS-type. To address this issue, a time-series-based CCS-type MF-PCC strategy is proposed in this paper for a permanent magnet synchronous motor (PMSM) driving system. By the online building and updating time-series model based on the sampled data, the plant is accurately expressed, and the control strategy is easily realized in the CCS-type.
Firstly, this approach establishes a time-series model and updates the regressive vector summarizing input and output signals based on sampled data. Secondly, all undetermined coefficients in the model are estimated through the recursive least square (RLS) algorithm. This way, the current operating state is described as a discrete-time transfer function within the model. Finally, according to predictive reference by the Lagrange algorithm, the regressive vector is updated to predict the output signal and generate the control law. This time-series model is easily realized in the CCS-type with good accuracy, addresses the problem of the complex calculation process, and fully eliminates all time-varying physical parameters and their influences in the a priori model of the plant.
Simulation and experimental results on a PMSM driving system show that the proposed method resists disturbances and tracks the reference successfully to suit electric vehicle driving operations, in which the disturbances mainly include changed parameter mismatches, load torque and DC voltage. Continuous Fourier analysis and accumulated error comparisons between different control strategies demonstrate that the proposed method achieves a range of total harmonic distortion (THD) between 3.3%-3.9%, with an average value of 3.53%. Compared to conventional strategies, the proposed method has the minimum ascending slope of accumulated error of current. These results demonstrate the effectiveness of the time-series model and the convergence of the estimation algorithm. Additionally, to analyze the impact of system noise on driver perception, the proposed method is tested under different speed references and load torques. Finally, to validate the achieved robustness, experimental results are obtained with different parameter mismatches of typical physical parameters, including stator resistance, stator inductance, and magnet flux linkage, and compared using continuous Fourier analysis.
The following conclusions can be drawn from the validations: 1) The proposed method represents the current operating state of the motor driving system as a time-series model in the CCS-type. Compared with the conventional MF-PCC strategy using ultra-local, it formulates the system as a group of discrete-time transfer functions. 2) Compared with conventional MF-PCC strategies using ultra-local, the obtained current quality, dynamics and system noises are obviously improved due to the good accuracy of the time-series model. 3) The time-series model replaces the a priori model, which includes multiple time-varying physical parameters of the system, since the modeling and updating processes of the time-series model are directly based on the designed estimation algorithm and sampled data. 4) The orders of the model should be selected comprehensively, considering the permitted calculation time and performances to prevent overrun error or insufficient accuracy.

Key words： Model-free predictive control time series model least square estimation algorithm current prediction

PACS:

TM341

基金资助:

国家自然科学基金（52277070）和福建省科技计划资助项目（2022T3070）

通讯作者: 汪凤翔, 男,1982年生,博士,教授,研究员,博士生导师,研究方向为电力电子与电力传动。E-mail：fengxiang.wang@fjirsm.ac.cn

作者简介: 魏尧, 男,1993年生,博士,博士后,研究方向为新能源汽车电控系统、交流电机伺服系统及其先进控制。E-mail：yao.wei@fjirsm.ac.cn

引用本文:

魏尧, 柯栋梁, 黄东晓, 汪凤翔, 康劲松. 基于时间序列的永磁同步电机连续控制集无模型预测电流控制[J]. 电工技术学报, 0, (): 8921-. Yao Wei, Dongliang Ke, Dongxiao Huang, Fengxiang Wang, Jinsong Kang. A Continuous-Control-Set Type Model-Free Predictive Current Control based on Time-Series for PMSM Drives. Transactions of China Electrotechnical Society, 0, (): 8921-.

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