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<p>To address the challenge of balancing high-precision control and low power consumption in active magnetic bearing (AMB) system, a low-power precise controller (LPPC), which includes Kalman filter, LQR control, and adaptive parameter control, was designed. First, the rotor dynamics model of the AMB system was formulated. A Kalman filter was implemented to estimate the system states with reduced observation noise and thereby improved accuracy. To reduce the control current consumption while ensuring optimal stabilization performance, the linear quadratic regulator (LQR) was designed. Furthermore, a variable parameter control strategy was proposed to dynamically adjust control parameters, enhancing the system adaptability. Finally, experimental evaluations were conducted. Compared with LQR control, LPPC control reduces the average control current by 39.0% in the step experiment, the average tracking error by 67.2% in the sine tracking experiment, and the fluctuation amplitude by 43.9% in the pulse interference experiment, proving that LPPC control exhibits low power consumption, fast response, and high control accuracy.</p>

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A low-power precise control of active magnetic bearing system based on dynamic LQR and Kalman filter

  • 期刊论文

  • 发表日期：

    2025年2月8日

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  • Lin Li,
  • Xiangbo Xu,
  • Junfeng Li,
  • Bolun Li,
  • Hao Zeng

摘要

To address the challenge of balancing high-precision control and low power consumption in active magnetic bearing (AMB) system, a low-power precise controller (LPPC), which includes Kalman filter, LQR control, and adaptive parameter control, was designed. First, the rotor dynamics model of the AMB system was formulated. A Kalman filter was implemented to estimate the system states with reduced observation noise and thereby improved accuracy. To reduce the control current consumption while ensuring optimal stabilization performance, the linear quadratic regulator (LQR) was designed. Furthermore, a variable parameter control strategy was proposed to dynamically adjust control parameters, enhancing the system adaptability. Finally, experimental evaluations were conducted. Compared with LQR control, LPPC control reduces the average control current by 39.0% in the step experiment, the average tracking error by 67.2% in the sine tracking experiment, and the fluctuation amplitude by 43.9% in the pulse interference experiment, proving that LPPC control exhibits low power consumption, fast response, and high control accuracy.