磁流变阻尼器的神经网络建模及应用

The Neural Network Modeling of MR Dampers and Application

对非线性的磁流变阻尼器进行建模,建立单个磁流变阻尼器的神经网络正模型和神经网络逆模型,设计了两种四磁流变阻尼器的神经网络逆模型,通过两层控制策略将它们应用于汽车半主动悬架控制.结果表明:第1种神经网络逆模型对簧载质量的垂直加速度、俯仰角加速度和侧倾角加速度具有较好的控制效果,可有效改善车辆的行驶平顺性和操纵稳定性;第2种神经网络逆模型还有待改善.

In order to model the nonlinear MR damper, the neural network models for the direct model and the inverse model of a single MR damper are created respectively. On this basis, two different recurrent neural network （ RNN ） inverse models for four MR dampers are designed and applied to the control of the semi-active suspension of the full-vehicle model. The simulation results demonstrate that the first RNN inverse model can greatly reduce the vertical acceleration and pitch angular acceleration and roll angular acceleration of the sprung-mass so that the ride comfort and handling of the semi-active suspension can be dramatically improved. However, there is still room for improvement for the second RNN inverse model.