Experimental forward and inverse modelling of magnetorheological dampers using an optimal Takagi–Sugeno–Kang fuzzy scheme

Abstract

An evolving encoding scheme is presented in this article for a fuzzy-based nonlinear system identification scheme, using the subtractive fuzzy C-mean clustering and a modified version of non-dominated sorting genetic algorithm. This method is able to automatically select the best inputs as well as the structure of the fuzzy model such as rules and membership functions. Moreover, three objective functions are considered to satisfy both accuracy and compactness of the model. The developed method is then employed to identify both forward and inverse models of a highly nonlinear structural control device, that is, magnetorheological damper. Experimental results showed that the proposed evolving Takagi–Sugeno–Kang fuzzy model can identify and grasp the nonlinear behaviour of magnetorheological damper very well with minimal number of inputs and fuzzy rules.