Parameters identification of magnetorheological damper based on improved grey wolf optimization algorithm

Abstract Parameter identification of the mechanical model of magnetorheological dampers is usually carried out through a combination of optimization algorithms, but there is little research on the impact of the optimization algorithm itself on the identification results. In order to improve the accuracy of parameter identification results, the influence of the parameters of the optimization algorithm itself on the fitting results was investigated in this study, and the optimization algorithm adopted the improved grey wolf algorithm. The effects of different wolf pack numbers and iteration times on recognition results were examined in this study. The various parameters in the mechanical model of magnetorheological dampers are determined utilizing the optimal combination of algorithm parameters. Finally, the validity of the identification results was verified by evaluating the consistency between the identified damping force and the experimental damping force. The results indicate that when the optimal combination is used, the accuracy of parameter identification can be improved.