volume | PIER Journals

Abstract

To address the inherent trade-off between convergence speed and steady-state error in conventional least mean square (LMS) algorithms for inverter-driven six-pole active magnetic bearings (AMBs), this paper proposes an online adaptive vibration compensation control strategy. In this strategy, a fuzzy-optimized Rayleigh-distribution-curve hybrid-weighted coefficient LMS (RDC-HWCLMS) algorithm is integrated with the influence coefficient method (ICM). First, the rotor unbalance dynamic model is derived, and the LMS-based synchronous vibration extraction principle is analyzed. Second, a Rayleigh-based variable-step-size law is constructed to improve transient convergence, and a hybrid-weighted coefficient update is introduced to suppress steady-state weight fluctuations under noisy displacement measurements. Furthermore, a fuzzy inference system (FIS) is introduced to enhance self-adaptability by dynamically tuning the step-size scaling parameter according to real-time error states. The extracted synchronous vibration components are then used by the ICM to generate compensation current references, which are injected into the AMB control system through the three-phase inverter. Finally, comparative simulated and experimental results on a 4-DOF inverter-driven six-pole AMB platform verify that the proposed online adaptive compensation control method achieves faster convergence, lower residual vibration, and stronger dynamic tracking adaptability than conventional LMS algorithms.

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Dr. Zhihao Ma

Dr. Yichen Liu

Prof. Huangqiu Zhu