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
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.
