Abstract:
In this paper, the local linear models of a magneto-rheological (MR) damper are obtained
based on the Takagi-Sugeno (T-S) fuzzy modelling approach. In these local linear models, the
output force of the MR damper is expressed as the linear summation of the state variables
(relative displacement and relative velocity) and input voltage. To obtain these local linear
models with high accuracy, the genetic algorithm (GA) with a new encoding method is
applied to search for the optimal model parameters. The proposed hybrid intelligence
technique can evolve the fuzzy rule structure (number of rules and selection of rules) and the
input structure (number of premise inputs and selection of premise inputs) simultaneously so
that the obtained linear models have the simplest structures without decreasing the modelling
accuracy. To validate the proposed approach, the modelling errors between the MR damper
output and the corresponding linear model output are compared for the given number of rules
case and for the automatically selected rules case with using three different selection
approaches for the premise input variables. It is confirmed by the validation results that the
proposed hybrid intelligence technique can find the optimal linear model for the MR damper.
