发表机构
School of Automation Beijing Institute of Technology（北京理工大学自动化学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于ESO扰动前馈和数据驱动H∞残差反馈的PMSM无模型电流控制方法，无需参数辨识，通过强化学习学习残差策略，实现快速跟踪和强鲁棒性。
AI 中文摘要
本文提出了一种基于扰动前馈和残差反馈的永磁同步电机（PMSM）无模型电流控制方法。超局部电流模型将电机动力学、参数不确定性、交叉耦合效应及其他非理想因素纳入广义集总扰动中。扩展状态观测器（ESO）估计这些集总扰动，并通过前馈作用对其进行补偿，将原始PMSM电流控制问题转化为对简化后补偿残差系统的调节问题。随后，利用离策略积分强化学习直接从运行数据中学习残差系统的状态反馈H∞控制器。由于残差动力学简化，值函数和控制策略以二次和线性形式参数化，将学习问题降维为低维参数估计，无需神经网络逼近。所提方法既不需要PMSM电气参数的先验知识，也不需要在线辨识：输入增益失配被纳入ESO估计的集总动力学中，而残差反馈策略则从运行数据中获得。与无差拍预测电流控制、基于模型的H∞控制和无模型预测电流控制的对比仿真表明，该方法具有快速的电流跟踪、较低的电流畸变以及对大参数变化的强鲁棒性。在学习的H∞策略固定且不进行重新训练或重新调整的情况下，当定子电阻、定子电感和永磁体磁链同时变化至其标称值的20%和200%时，控制性能几乎保持不变。
英文摘要
This paper proposes a model-free current control method for permanent magnet synchronous motors (PMSMs) based on disturbance feedforward and residual feedback. An ultra-local current model incorporates motor dynamics, parameter uncertainties, cross-coupling effects, and other nonideal factors into generalized lumped disturbances. An extended state observer (ESO) estimates these lumped disturbances and compensates for them through feedforward action, transforming the original PMSM current-control problem into regulation of a simplified post-compensation residual system. A state-feedback H-infinity controller for the residual system is then learned directly from operating data using off-policy integral reinforcement learning. Owing to the simplified residual dynamics, the value function and control policies are parameterized in quadratic and linear forms, reducing the learning problem to low-dimensional parameter estimation without neural-network approximation. The proposed method requires neither prior knowledge nor online identification of PMSM electrical parameters: input-gain mismatch is incorporated into the ESO-estimated lumped dynamics, while the residual-feedback policy is obtained from operating data. Comparative simulations against deadbeat predictive current control, model-based H-infinity control, and model-free predictive current control show fast current tracking, low current distortion, and strong robustness to large parameter variations. With the learned H-infinity policy fixed and without retraining or retuning, nearly unchanged control performance is maintained when stator resistance, stator inductance, and permanent-magnet flux linkage are simultaneously varied to 20% and 200% of their nominal values.
Comments20 pages, 14 figures
