An iterative based approach for hysteresis parameters estimation in Magnetorheological dampers

The following work entails the problem of regenerating the hysteresis loop in the Magnetorheological (MR) dampers. The collected data from tests are not sufficient neither efficient for designing optimal controls compensating for the hysteresis in the dampers. This work presents an iterative based approach for estimating the hysteresis parameters, the method however can be generalized for different kind of dampers or actuators hence the hysteresis loop can be generalized using available test data. Some assumptions can be introduced in order to facilitate the underlines of the parameters estimation, one of the assumptions in this work is to use predetermined hysteresis parameters and regenerate the actual data using continuous state space model (SSM). The SSM can be based on verified models like Bouc-Wen, Lugre or Dahl models. In this work, Bouc-Wen model is used to generate the actual hysteresis data. The core of this work is to use the iterative approach based on Particle Swarm Optimization (PSO). The PSO algorithm is used along with other algorithms like the Root Mean Square (RMS) error which is used to evaluate the convergence of the results at each of the iterations for a defined number of iterations. The trade-off relation is the basis of evaluation when using the PSO based algorithm, dependency on initial guessing, number of iterations and desired estimation accuracy. However, the PSO algorithm tend to estimate the hysteresis parameters close enough to generate the actual hysteresis enabling a ground to develop similar algorithms for different kind of actuators or dampers.