Online Vibration Control for Active Magnetic Bearing-Based Motor Drive With Improved Gray Wolf Optimization

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This article presents a novel online vibration suppression strategy to mitigate the dominant vibration issues in active magnetic bearing (AMB)-based permanent magnet synchronous motors, particularly those related to the rotational frequency spectrum. A dynamic axial displacement control (DADC) method is introduced to address the degradation in AMB control accuracy caused by machining errors. The study also investigates the impact of machining-induced inaccuracies on axial displacement sensor readings, providing insights for the design of dynamic DADC signals. To further enhance the adaptability of the DADC algorithm, an online vibration suppression strategy is developed, which integrates real-time vibration spectral amplitude extraction using the adaptive gradient linear neuron (AdaGrad-LN) method and an improved gray wolf optimizer with evolutionary population dynamics (GWO-EPD) for self-tuning the DADC control parameters. Compared with conventional GWO, the proposed GWO-EPD enhances population diversity by eliminating poorly performing individuals and repositioning them, thereby improving convergence speed and tuning accuracy. Experimental results demonstrate the effectiveness of the proposed approach. The GWO-EPD-based online vibration control achieves a 93.1% reduction in vibration amplitude and a 23 dB decrease in vibration acceleration compared with traditional AMB vibration control methods.