MODEL OF MAGNETORHEOLOGICAL DAMPER BASED ON SYSTEM IDENTIFICATION

Magnetorheological (MR) damper is one of the more promising new devices for semi-active control of structures. External energy required by the adjustable fluid damper is minuscule while the damper can produce great force on the order of milliseconds. The characteristics of MR damper have been described by a set of nonlinear differential equations including three physical parameters such as displacement, voltage and force. When displacement and voltage are input into MR damper, the device can generate force. In this paper, the performance of MR damper is simulated based on system identification with optimal multi-layer perceptron neural networks and ARX model. The trained optimal networks can accurately predict force by a forward model and voltage by an inverse model. If the neural networks are used in a control system, the semi-active MR damper can be easily used for semi-active control of structures.