Evolutionary Takagi-Sugeno Fuzzy Modelling for MR Damper

This paper presents an approach for learning the Takagi-Sugeno (T-S) fuzzy model by Genetic Algorithm (GA). In this approach, the fuzzy rule structure is encoded by binary code in the chromosome in which the position of 1 indicates the selected rules and the sum of 1 indicates the number of rules. The membership function (MF) parameters (centres and bases) are evolved by GA in combining with the pseudo-inversion algorithm for obtaining the consequent parameters. The sum of squared error (SSE) between the true output and the T-S model prediction is used as objective function. Then, this approach is applied to the modelling of dynamic behaviour of a magneto-rheological (MR) damper which shows highly nonlinear characteristics due to hysteretic phenomenon. It is shown by the validation test that the developed T-S fuzzy model can represent the dynamic behaviour of the MR damper satisfactorily.