Parameter identification of Magnetorheological damper using particle swarm optimization

Particle swarm optimization (PSO) technique has achieved a considerable success in solving nonlinear, nondifferentiable, multi-modal optimization problems. Currently, PSO is broadly applied in several scientific and engineering optimization applications. This paper introduces an identification of magnetorheological (MR) damper’s parameters using the PSO algorithm to introduce a more simple and accurate model. The proposed model predicts the MR damper force as a nonlinear function of the damper velocity, acceleration and command voltage to the damper coil, without using any complex differential equations, which will be very beneficial for complicated systems. PSO algorithm aims to minimize the rootmean-square-error of the damping force between the proposed model and the modified Bouc-Wen model which can estimate the dynamic behavior of the MR damper precisely. The validation of the proposed model is achieved by comparing its behavior against the behavior of the modified Bouc-Wen model. The validation results clearly reflect that the use of the proposed model can dependably predict the dynamic response of the MR damper as a nonlinear function of damper velocity, acceleration and command voltage. Keywords—MR damper, modified Bouc-Wen model, PSO, parameter identification