摘要:磁流变阻尼器是一种新型的智能振动控制装置。通过磁流变阻尼器的性能试验,研究了在不同电流输入下阻尼力-位移、阻尼力-速度之间的关系,分析了摩擦型磁流变阻尼器的主要特点。采用BP神经网络,建立了磁流变阻尼器的正向模型和逆向模型。仿真结果显示,神经网络模型能准确地预测磁流变阻尼器的阻尼力和控制电流,证明该方法的有效性。与已有的模型相比,具有精度高,计算简便等特点。
Abstract: Magnetorheological damper is a new kind of smart device for vibration control. Through characteristic testing of MR damper, force-displacement and force-velocity relationship at different current is researched; the main features of friction-type MR damper are analyzed. A forward model and an inverse model of MR damper are established by using BP neural network. Simulation results show that the neural network model can accurately predict force and current of MR damper, so the approach is effective. Comparing with the existing model, the features of the model built in this paper are high accuracy and easy calculation.
*国家自然科学基金项目(50578063) 国家自然科学重点项目(50738002)