Dynamic Characteristics Identification of Magnetic Rheological Damper Based on Neural Network

Magnetic rheological (MR) damper, as today´s new semi-active control device, is widely used in vibration control engineering. However, in most control methods the controller´s dynamic characteristics need to be known in advance. Because of highly nonlinear characteristics of MR damper, it is very difficult to establish its mathematical model to describe the reverse dynamic characteristics, which is essential in achieving the overall control strategy. In this paper, based on the identification role of neural network in complex nonlinear systems, according to performance tests of MR damper, the dynamic and inverse dynamic characteristic neural network model of MR damper is established, and the analysis and comparison of the neural network model conclusions and experimental conclusions are given, the results show that the neural network model of MR damper dynamic characteristics is reliable and effective.