Iterative Learning Pre-Compensation Strategy Based Active Disturbance Rejection Position Control Method of Permanent Magnet Linear Synchronous Motor Drives
Liang Wanjia1, Huang Xuzhen1, Wang Anpeng2
1. College of Automation Nanjing University of Aeronautics and Astronautics Nanjing 211100 China; 2. School of Electrical Engineering and Automation Harbin Institute of Technology Harbin 150001 China
Abstract：The permanent magnet linear synchronous motor (PMLSM) has gained significant adoption in linear servo drive applications such as precision CNC machine tools due to its advantages of the direct drive mechanism, simplified structure, and high transmission efficiency. However, the inherent thrust ripple induced by the cogging effect and end effects in PMLSM systems adversely affects the tracking performance of servo mechanisms and machining accuracy, making thrust ripple suppression crucial. This paper presents a novel thrust ripple suppression method employing an iterative learning pre-compensation (ILPC) strategy to improve system control performance, thereby achieving high-dynamic and high-precision operation for PMLSM-driven systems. Initially, a comprehensive modeling and analysis of the PMLSM and its thrust fluctuations are conducted. A two-degree-of-freedom controller is designed, comprising an acceleration feedforward controller (AFFC), a feedback controller, and a disturbance observer. This configuration preliminarily mitigates errors and thrust fluctuations during the acceleration and deceleration phases. The stability of the PI-Lead controller is validated through open-loop Bode plots. A comparative analysis assesses the impact of disturbance observers with varying orders and bandwidths on the system's disturbance rejection capabilities. Subsequently, an anticipatory iterative learning controller (AILC) is introduced. The convergence of the AILC is rigorously demonstrated, and parameter tuning principles are investigated. The iterative learning process is leveraged to suppress thrust fluctuations further and enhance the system's tracking accuracy. Additionally, the equivalent current observation error of the PMLSM's detent force disturbance is fitted and pre-compensated, thereby reducing the dependency of iterative learning control on motion modes. Finally, an experimental platform for PMLSM based on STM32F407 is established. The experimental results demonstrate that the proposed ILPC strategy effectively suppresses thrust fluctuations in PMLSM across different operating conditions. Specifically, under no-load conditions, the root mean square (RMS) value of position tracking error is reduced by more than 15% at three different speed setpoints. Under light-load conditions, the RMS value of position tracking error consistently reduces, exceeding 10%.
梁万佳, 黄旭珍, 王安鹏. 基于迭代学习预补偿策略的永磁直线同步电机推力波动抑制方法[J]. 电工技术学报, 2025, 40(16): 5247-5258.
Liang Wanjia, Huang Xuzhen, Wang Anpeng. Iterative Learning Pre-Compensation Strategy Based Active Disturbance Rejection Position Control Method of Permanent Magnet Linear Synchronous Motor Drives. Transactions of China Electrotechnical Society, 2025, 40(16): 5247-5258.
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