基于超局部化时间序列的永磁同步电机无模型预测电流滑模控制策略

doi:10.19595/j.cnki.1000-6753.tces.230301

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摘要

在复杂环境、多变负载工况中,由于时变电感参数、磁场耦合、铁心饱和等影响,电机控制精度及可靠性下降。为解决如上问题,本文充分利用时间序列模型反映电机电压、电流等状态变量之间关系,消除传统参数化建模的影响,提出基于超局部化时间序列的无模型预测电流滑模控制（SMC）方法。该方法将时间序列数据驱动模型超局部化,提升传统超局部模型精度,并采用递归最小二乘（RLS）算法在线估计和更新模型中全部待定系数,实时精准响应当前系统工作状态。在此基础上,结合超局部时间序列模型,生成滑模控制函数,并设计Lyapunov方法验证函数趋近条件。实验结果表明,与传统控制方法相比,提出方法具有更强的鲁棒性、更优的电流质量和较低的系统噪声。

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关键词 ：无模型滑模控制, 超局部化时间序列模型, 递归最小二乘算法, 数据驱动模型

Abstract：

Considering the limited flexibility and robustness of the typical sliding mode control (SMC), it falls short in meeting the demands of a complex environment with variable load and the influence of time-varying inductance parameters, magnetic field coupling, core saturation, and other factors. On the contrary, the model-free SMC strategy proves to be more effective in overcoming these challenges, with the main concern being the accuracy of the model that affects control performance. To tackle this issue, this paper proposes a model-free predictive SMC strategy utilizing an ultra-localized time-series model for a permanent magnet synchronous motor (PMSM) driving system. By representing the motor as a collection of discrete-time linear functions and maintaining high model accuracy through an online estimation algorithm, the proposed strategy is better suited to the motion characteristics of the motor system.
Firstly, this approach establishes an ultra-localized time-series model and updates the regressive vector, which summarizes input and output signals based on sampled data only. Secondly, all undetermined coefficients in the model are estimated using the recursive least square (RLS) algorithm. Consequently, the current operating state of the motor driving system is described as a collection of discrete-time linear functions and converted into the ultra-local structure to generate the sliding mode signal. Finally, the control functions are designed based on the power reaching rule, and the reaching conditions are verified using the Lyapunov method. This ultra-localized time-series model is easily implemented within the SMC strategy, offering good accuracy and addressing the issues caused by time-varying physical parameters in the plant’s model of the typical SMC and input gain of the conventional observer-based ultra-local model.
Simulation and experimental results on a PMSM driving system demonstrate the effectiveness of the proposed method in resisting disturbances and successfully tracking the reference. The disturbances primarily include changed parameter mismatches and load torque. Fourier analysis and accumulated error comparisons between the proposed and conventional model-free SMC strategies show that the proposed method improves current quality and model accuracy by reducing the total harmonic distortion (THD) of 3.14%. Compared to the conventional strategy, the proposed method exhibits the minimum ascending slope of accumulated error for current and lower operating noise amplitudes in various speed references and load torques. These results highlight the effectiveness and convergence of the ultra-localized time-series model-based SMC strategy with the estimation algorithm. To further validate its robustness, experimental results are obtained with different parameter mismatches of the stator inductance, and are compared using continuous Fourier analysis calculations.
The validations provide the following conclusions: 1) The proposed method adopts an ultra-localized time-series model to represent the current operating state of the motor driving system. Unlike the conventional strategy that utilizes an ultra-local approach, this model formulates the system as a collection of discrete-time linear functions. 2) The ultra-localized time-series model significantly improves current quality, accumulated current error, and system noise compared to the conventional strategy. This improvement is attributed to the high accuracy of the ultra-localized time-series model and the eliminated influences of the unsuitable input gain. 3) By employing a designed estimation algorithm and sampled data, the ultra-localized time-series model replaces the physical model, which involves multiple time-varying physical parameters of the system. This replacement enables more accurate modeling and updating processes.

Key words： Model-free sliding mode control ultra-localized time series model recursive least square algorithm data-driven model

PACS:

TM341

基金资助:

国家自然科学基金（52277070）,中国博士后科学基金（2023M730598）和福建省科技计划资助项目（2021I0039）

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, 张祯滨. 基于超局部化时间序列的永磁同步电机无模型预测电流滑模控制策略[J]. 电工技术学报, 0, (): 239610-239610. Yao Wei, Dongliang Ke, Dongxiao Huang, Fengxiang Wang, Zhenbin Zhang. Model-Free Predictive Sliding Mode Control Using Ultra-Localized Time-Series for PMSM Drives. Transactions of China Electrotechnical Society, 0, (): 239610-239610.

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