Parameter identification is a critical factor influencing the dynamic performance of permanent magnet synchronous motor (PMSM)-driven servo systems. However, mechanical parameter identification encounters the following challenges: multiple parameters that are significantly mutually coupled and difficulties in direct measurement, especially with friction parameters. Currently, most scholars have navigated these challenges through partial parameter identification, either by ignoring Coulomb friction, neglecting the dynamic processes of Coulomb friction, or identifying only the parameters associated with the friction model. In this study, a scheme using a parallel-cascaded extended sliding-mode observer (PCESMO) is proposed to address these challenges. The PCESMO relies solely on phase currents and rotor position to identify parameters, including the moment of inertia, viscous damping, load torque, and parameters of a simplified LuGre model that considers the dynamic process of Coulomb friction during self-commission. Additionally, the PCESMO has low computational complexity and is easy to implement. The effectiveness of the PCESMO was validated through both simulations and experiments. Compared to other competitive methods, PCESMO-identified parameters result in optimal control effects.

中文翻译：

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PMSM 驱动伺服系统自调试期间的并行级联参数识别方案


参数辨识是影响永磁同步电机（PMSM）驱动伺服系统动态性能的关键因素。然而，机械参数识别面临以下挑战：多个参数相互耦合显着，难以直接测量，尤其是摩擦参数。目前，大多数学者通过部分参数识别来应对这些挑战，要么忽略库仑摩擦，忽略库仑摩擦的动态过程，要么仅识别与摩擦模型相关的参数。在本研究中，提出了一种使用并行级联扩展滑模观测器（PCESMO）的方案来解决这些挑战。 PCESMO 仅依靠相电流和转子位置来识别参数，包括转动惯量、粘性阻尼、负载扭矩以及考虑自调试期间库仑摩擦动态过程的简化 LuGre 模型的参数。此外，PCESMO 计算复杂度低且易于实现。通过模拟和实验验证了 PCESMO 的有效性。与其他竞争方法相比，PCESMO 确定的参数可实现最佳控制效果。