It is important to optimize control parameters for the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. In this article, a multi-optimization objective online tracking (MOOT)-based parameter self-tuning method is proposed to improve the sensorless control performance when the high-frequency signal injection (HFSI) method is used, which involves both offline and online stages. By the parameter self-learning and the model analysis, the injection signal, the position observer, the speed, and current regulators are preliminarily designed offline to ensure that the system can start smoothly. Based on the parameter characteristics analysis, an adaptive law with the speed ripple and torque ripple as the optimization index is designed to realize the online tracking of control and observation parameters. To solve the issues of tuning delay and mistuning, a moving window-based error detection strategy is investigated, which improves the parameter self-tuning speed and accuracy. The proposed method can improve the steady- and transient-state performance and realize the sensorless debug-free operation for HFSI-based method. The effectiveness is verified by experiments on a 2.2-kW PMSM drive platform.

中文翻译：

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基于多优化目标的无感永磁同步电机驱动参数在线跟踪自整定方法

优化控制参数对于无位置传感器永磁同步电机 (PMSM) 驱动器的高性能运行非常重要。在本文中，提出了一种基于多优化目标在线跟踪（MOOT）的参数自调整方法，以提高使用高频信号注入（HFSI）方法时的无传感器控制性能，该方法涉及离线和在线阶段. 通过参数自学习和模型分析，离线初步设计了喷射信号、位置观测器、速度、电流调节器，确保系统顺利启动。在参数特性分析的基础上，设计了以速度脉动和转矩脉动为优化指标的自适应律，实现控制和观测参数的在线跟踪。针对整定延迟和失谐问题，研究了一种基于移动窗口的错误检测策略，提高了参数自整定的速度和精度。所提出的方法可以提高稳态和瞬态性能，并实现基于 HFSI 的方法的无传感器免调试操作。通过在 2.2 kW PMSM 驱动平台上进行的实验验证了有效性。