NONPARAMETRIC MODELING OF MAGNETORHEOLOGICAL DAMPER
With the unique advantages such as low power requirement and adequately fast response rate, Magneto-Rheological (MR) dampers have been employed in vibration control for civil engineering structures. An accurate and efficient model of MR damper is needed for effective control system design. Even several mathematical parametric models of MR damper have been proposed in the last years, it is still a challenging problem to model the behaviour of MR damper accurately.Moreover, some existing parametric models are too complex for control system design. Because of its nonlinear mapping capability, parallel computation, and adaptability, artificial neural network (ANN)provides an alternative way to describe the actual performance of MR dampers. In this study, two types of three-layer neural networks with different input variables were constructed respectively to model a MR damper at different excitation currents using teat data. The performance of the two proposed ANN based models is validated. The results show that the predicted damping forces of the two neural network models match well with the forces measurement, which demonstrates that the proposed neural network model can provide a computationally efficient way to model the behaviour of a MR damper.
MR damper Neural network Non-parameter model Force Displacement Velocity
Zhi-Gang Huang Bin Xu Zach Feinstein Shidey J. Dyke
College of Civil Engineering, Hunan University, Changsha 410082, P.R. China College of Civil Engineering, Hunan University, Changsha 410082, P.R. China Key Laboratory of Buildi Department of Civil Engineering, Washington University, St. Louis, MO 63130, USA
国际会议
第十届国际结构工程青年学者研讨会(The Tenth International Symposium on Structural Engineering for Young Experts)
长沙
英文
1860-1865
2008-10-19（万方平台首次上网日期，不代表论文的发表时间）