Comparison between Different Modelling Methods of Secondary Path to Maximize Control Effect for Active Engine Mounts

Active engine mount (AEM) is an effective approach which can optimize the noise, vibration and harshness (NVH) performance of vehicles. The filtered-x-least-mean-squares (FxLMS) algorithm is widely applicated for vibration attenuation in AEMs. However, the performance of FxLMS algorithm can be deteriorated without an accurate secondary path estimation. First, this paper models the secondary path using finite impulse response (FIR) model, infinite impulse response (IIR) model and back propagation (BP) neural network model and the model errors of which are compared to determine the most accurate and robust modeling method. After that, the influence of operation frequency on accuracy of the secondary path model is analyzed through simulation approach. Then, the impact of reference signal mismatch on the control effect is demonstrated to study the robustness of FxLMS algorithm. Finally, a test bench is established to optimize other control parameters and validate the performance of the improved FxLMS control algorithm. The experiment result indicates that the FxLMS control algorithm combined with optimized parameters ensures excellent performance on active vibration isolation and can quickly adapt to the changes of excitation.