Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models

The nonlinearity behaviour of magnetorheological fluid (MRF) can be described using a number established models such as Bingham and Modified Bouc-Wen models. Since these require the identification model parameters, there is need to estimate parameters' value carefully. In this paper, an optimization algorithm, i.e., Particle Swarm Optimization (PSO) utilized identify models' parameters. PSO algorithm distinctively controls best fit by minimizing marginal error through root-mean-square between empirical response. validation attained comparing resulting modified against same model's behaviour, identified Genetic Algorithm (GA). results indicate that application better in identifying Results from estimation used design controller for various applications prosthetic limbs.