Parameter identification of the phenomenological model for magnetorheological fluid dampers using hierarchic enhanced particle swarm optimization

Abstract

The magnetorheological (MR) fluid damper, as a new generation of high-performance and intelligent vibration damping devices for engineering structures, has broad application prospects in structural vibration reduction. Nonlinear hysteresis is a complex phenomenon of MR dampers. In this paper, the phenomenological model is used to simulate the dynamics of MR dampers. To accurately and efficiently identify the parameters of the phenomenological model, the hierarchical enhanced particle swarm optimization (HEPSO) algorithm is proposed. This algorithm applies a hierarchical optimization mechanism and introduces media particles to the enhanced PSO (EPSO) algorithm to improve the optimization process of parameter identification and enhance the performance of the PSO algorithm without reducing the possibility of finding the optimal solution. Compared with the standard PSO (SPSO) algorithm and the EPSO algorithm, the HEPSO algorithm has a higher efficiency, accuracy and robustness in identifying a highly nonlinear hysteretic system problem.