With the revival of magnetorheological technology research in the 1980’s, its application in vehicles is in- creasingly focused on vibration suppression. Based on the importance of magnetorheological damper modeling,...With the revival of magnetorheological technology research in the 1980’s, its application in vehicles is in- creasingly focused on vibration suppression. Based on the importance of magnetorheological damper modeling, non- parametric modeling with neural network, which is a promising development in semi-active online control of vehicles with MR suspension, has been carried out in this study. A two layer neural network with 7 neurons in a hidden layer and 3 inputs and 1 output was established to simulate the behavior of MR damper at different excitation currents. In the neural network modeling, the damping force is a function of displacement, velocity and the applied current. A MR damper for vehicles is fabricated and tested by MTS; the data acquired are utilized for neural network training and vali- dation. The application and validation show that the predicted forces of the neural network match well with the forces tested with a small variance, which demonstrates the effectiveness and precision of neural network modeling.展开更多
Severe vibration of underground structures may be induced under blast loads. According to the characteristics of the explosion-induced ground shock wave, a new-type damper, inverse control magneto-rheological（MR） da...Severe vibration of underground structures may be induced under blast loads. According to the characteristics of the explosion-induced ground shock wave, a new-type damper, inverse control magneto-rheological（MR） damper was designed to control the vibration, The high-frequency performance test of the MR damper was carried out on the small shaking table. It is shown that the performance can be modeled by use of the modified Bouc-Wen model, and the Parameters of the model keep stable in the range of 15--50 Hz.展开更多
基金Projects 50135030 and 60404014 supported by National Natural Science Foundation of China
文摘With the revival of magnetorheological technology research in the 1980’s, its application in vehicles is in- creasingly focused on vibration suppression. Based on the importance of magnetorheological damper modeling, non- parametric modeling with neural network, which is a promising development in semi-active online control of vehicles with MR suspension, has been carried out in this study. A two layer neural network with 7 neurons in a hidden layer and 3 inputs and 1 output was established to simulate the behavior of MR damper at different excitation currents. In the neural network modeling, the damping force is a function of displacement, velocity and the applied current. A MR damper for vehicles is fabricated and tested by MTS; the data acquired are utilized for neural network training and vali- dation. The application and validation show that the predicted forces of the neural network match well with the forces tested with a small variance, which demonstrates the effectiveness and precision of neural network modeling.
基金Supported by National Nature Fund and National Civil-Defense Office
文摘Severe vibration of underground structures may be induced under blast loads. According to the characteristics of the explosion-induced ground shock wave, a new-type damper, inverse control magneto-rheological（MR） damper was designed to control the vibration, The high-frequency performance test of the MR damper was carried out on the small shaking table. It is shown that the performance can be modeled by use of the modified Bouc-Wen model, and the Parameters of the model keep stable in the range of 15--50 Hz.
