Experimental study of Magneto-Rheological materials and its damper dynamic characteristics

As nowadays new semi-active control device, Magneto-Rheological (MR) damper is widely used in vibration control engineering. However, it is difficult to establish mathematical model to describe its reverse dynamic characteristics, because that MR damper has high nonlinear characteristics, but the model is very important in realizing whole control strategy. In this paper, MR damper force model which is convenient to realize engineering control is given, on this basis, the MR damper performance experiment and analysis is made, based on the identification effect of neural network in complex nonlinear system. The MR damper neural network positive dynamic and reverse dynamic characteristic model is put forward, the neural network model output results and experiment results are compared. The results show that damping force model proposed by the paper is easy to realize control and with high accuracy, meanwhile, the means of recognizing MR damper dynamic characteristics by neural network model is reliable and effective.