斜拉索振动的ER-MR阻尼器半主动神经网络控制

建立了采用ER—MR阻尼器作斜拉索的基于神经网络的半主动控制方法。该方法采用离线训练好的神经网络兼作系统状态观测器和系统控制器，并根据ER／MR阻尼器特点，引入面向速度剪切的半主动控制策略，实现神经网络对斜拉索进行在线带自反馈的半主动控制。数值试验的结果表明：采用神经网络进行半主动控制，能够达到很好的控制效果，整条斜拉索的振动都得到有效的抑制。

Semi-Active Neuro-Control of Stay Cables Using ER-MR Dampers

This paper reports a study on semi-active vibration control of stay cables using electro-magneto-rheological (ER-MR) dampers and adopting neural network control technique. Based on a finite element model of the cable, a neural network model is designed and trained to emulate the performance of the LQG controller. The trained neural network model is still a fully active controller but only a few response states are included as network inputs in the training process to simulate incomplete state observations. The fully active network controller is clipped to achieve the voltage value required to semi-actively control the cable vibration through ER-MR dampers. A numerical example of a 12 m-long stay cable specimen connected with a specifically designed low-force ER damper is provided to verify the effectiveness of the proposed control strategy.