. . "MR damper; modified Bouc-Wen model; PSO; parameter identification"@en . "*Parameter identification of Magnetorheological damper using particle swarm optimization" . "*Parameter identification of Magnetorheological damper using particle swarm optimization"@en . "*Parameter identification of Magnetorheological damper using particle swarm optimization"@en . "*Particle swarm optimization (PSO) technique has achieved a considerable success in solving nonlinear, non-differentiable, multimodal optimization problems. Currently, PSO is broadly applied in several scientific and engineering optimization applications. This paper introduces an identification of magnetorheological (MR) damper\u2019s parameters using the nonlinear 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 root-mean-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."@en . . . . . "P(LO1311)" . . "2014-11-16+01:00"^^ . "Birmingham" . . "*Particle swarm optimization (PSO) technique has achieved a considerable success in solving nonlinear, non-differentiable, multimodal optimization problems. Currently, PSO is broadly applied in several scientific and engineering optimization applications. This paper introduces an identification of magnetorheological (MR) damper\u2019s parameters using the nonlinear 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 root-mean-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." . "10.15224/978-1-63248-054-5-60" . . "6"^^ . "Institute of Research Engineers and Doctor" . "*Parameter identification of Magnetorheological damper using particle swarm optimization" . "RIV/68407700:21220/14:00227370!RIV15-MSM-21220___" . "Metered, Hassan Ahmed Mohamed" . "35722" . "[D04C93E16AD2]" . "Vampola, Tom\u00E1\u0161" . . . "RIV/68407700:21220/14:00227370" . "3905 State Street, Suite 7-31, Santa Barbara" . "21220" . . "CMS 2014 - Proceedings of the Second International Conference on Advances In Civil, Structural and Mechanical Engineering" . "\u0160ika, Zbyn\u011Bk" . "4"^^ . "El Sawaf, Ahmed" . "4"^^ . . . "978-1-63248-054-5" . . . . "El Sawaf, Ahmed" . . "Metered, Hassan Ahmed Mohamed" .