基于神经网络技术实现磁流变阻尼器对结构振动的优化控制(英文)

磁流变 ( MR)阻尼器是土木结构振动控制领域最有应用前景的半主动控制装置之一 ,但由于 MR阻尼器的高度非线性动特性 ,使得描述其逆向动特性的数学模型很难得到 ,即根据理想的阻尼力确定出 MR阻尼器所需的输入电压。然而 ,对基于 MR阻尼器的结构振动控制设计和仿真 ,这种反映阻尼力 -电压关系的逆向动特性模型是十分重要的。为此 ,本项研究针对 MR阻尼器的非线性特性 ,提出运用神经网络技术建立 MR阻尼器的神经网络模型来模拟其逆向动特性 ,神经网络模型的输出即为产生理想的阻尼力所需的输入电压。通过数值仿真结果探讨所提出的结构振动控制策略的适用性和有效性

NEURAL NETWORK REALIZATION OF STRUCTURAL VIBRATION CONTROL USING MR DAMPER

Magnetorheological (MR) dampers are one of the most promising new devices for civil infrastructural vibration control applications. However, due to their highly nonlinear dynamic behavior, it is very difficult to obtain of a mathematical model of inverse MR damper that has an explicit relationship between the desired damper force and the command signal (voltage). This force voltage relationship is especially required for the structural vibration control design and simulation using MR dampers. This paper focuses on using a neural network (NN) technique to emulate the inverse MR damper model. The output of the neural network can be used to command the MR damper for generating desired forces. Numerical simulations are also presented to illustrate the effectiveness of this inverse model in semi active vibration control using MR dampers.