Self-learning vibration absorber with negative electromagnetic stiffness for variable vibration

Abstract

Variable vibration is difficult to control due to the uncertainty and variability of vibration frequency. Vibration absorbers are widely used in engineering, however, the efficiency of mathematical design method decreases significantly once the external vibration changes. In order to suppress the variable frequency vibration, a novel self-learning tuning method for the vibration absorbers is proposed based on a large number of experimental vibration data, which contains the actual information of the optimal absorber parameters. The self-learning tuning method proposed can identify the external excitation frequency and label the optimal stiffness of the absorbers adaptively. In addition, model training can help achieve optimum vibration suppression through reasonable stiffness tuning for different variable low frequency excitation. Two vibration absorbers are designed with negative electromagnetic stiffness, and the effective frequency band can be decreased by applying current into the coil automatically. The vibration absorbers are slightly askew installed on the primary system to achieve bidirectional vibration attenuation. The experimental results indicate that the maximum vibration attenuation ratio is improved from 87.96% to 95.88% along Z-axis and 79.21% to 89.93% along X-axis in the bandwidth of 7–9 Hz.