论文标题
递归预测错误梯度算法和框架以在线识别PMSM参数
Recursive Prediction Error Gradient-Based Algorithms and Framework to Identify PMSM Parameters Online
论文作者
论文摘要
准确的机器参数的实时获取对于实现电动驱动器的高性能，尤其是针对关键任务应用程序的高性能。与饱和效应不同，温度变化很难预测，因此在线跟踪温度依赖性参数至关重要。在本文中，开发了一个统一的框架，用于在线参数识别旋转电机，前提是递归预测错误方法（RPEM）。其次，采用基于预测的梯度（$ \MATHBFψ^t $） - 用于识别温度敏感参数，即，内部永久磁铁同步机器（iPmsm）的永久磁铁通量链接（$ψ_m$）和定位磁力抗性（$ψ_M$）和定位磁力抗性（$ r_s $）。研究了三种算法，即随机梯度（SGA），高斯 - 纽顿（GNA）和物理解释性方法（Phyint）以进行估计增长计算。依赖速度的增益 - 划分方案用于将$ψ_m$和$ r_s $的相互依赖性解除。借助离线仿真方法，分析了RPEM的主要元素，例如$ \Mathbfψ^t $。概念验证和最佳算法的选择是通过使用基于芯片的系统（SOC）嵌入式实时模拟器（ERTS）制成的。随后，所选算法将借助3 kW的IPMSM驱动器验证，在该驱动器中，在基于SOC的工业嵌入式控制系统中实现了控制和估计例程。实验结果表明，总体而言，基于$ \Mathbfψ^t $的RPEM可以是在线和离线适应温度敏感参数的一种多功能技术。
Real-time acquisition of accurate machine parameters is of significance to achieving high performance in electric drives, particularly targeted for mission-critical applications. Unlike the saturation effects, the temperature variations are difficult to predict, thus it is essential to track temperature-dependent parameters online. In this paper, a unified framework is developed for online parameter identification of rotating electric machines, premised on the Recursive Prediction Error Method (RPEM). Secondly, the prediction gradient ($\mathbfΨ^T$)-based RPEM is adopted for identification of the temperature-sensitive parameters, i.e., the permanent magnet flux linkage ($Ψ_m$) and stator-winding resistance ($R_s$) of the Interior Permanent Magnet Synchronous Machine (IPMSM). Three algorithms, namely, Stochastic Gradient (SGA), Gauss-Newton (GNA), and physically interpretative method (PhyInt) are investigated for the estimation gains computation. A speed-dependent gain-scheduling scheme is used to decouple the inter-dependency of $Ψ_m$ and $R_s$. With the aid of offline simulation methods, the main elements of RPEM such as $\mathbfΨ^T$ are analyzed. The concept validation and the choice of the optimal algorithm is made with the use of System-on-Chip (SoC) based Embedded Real-Time Simulator (ERTS). Subsequently, the selected algorithms are validated with the aid of a 3-kW, IPMSM drive where the control and estimation routines are implemented in the SoC-based industrial embedded control system. The experimental results reveal that $\mathbfΨ^T$-based RPEM, in general, can be a versatile technique in temperature-sensitive parameter adaptation both online and offline.
