Hysteresis modeling of magneto-rheological damper using self-tuning Lyapunov-based fuzzy approach

Abstract

Magneto-rheological (MR) fluid damper is a semi-active control device that has recently received more attention because they offer the adaptability of active control devices without requiring the associated large power sources. But inherent nonlinear nature of the MR fluid damper is one of the challenging aspects for utilizing this device to achieve the high performance. So development of an accurate MR fluid damper model is necessary to take the advantages from its unique characteristics. The focus of this paper is to develop an alternative method for modeling a MR fluid damper by using a so-called self-tuning Lyapunov-based fuzzy model (STLFM). Here, the model is constructed in the form of a center average fuzzy interference system, of which the fuzzy rules are designed based on the Lyapunov stability condition. In addition, in order to optimize the STLFM, the back propagation learning rules are used to adjust the fuzzy weighting net. Firstly, experimental data of a damping system using this damper is used to optimize the model. Next, the optimized model is used to estimate online the damping performance in the real-time conditions. The modeling results prove convincingly that the developed model could represent satisfactorily the behavior of the MR fluid damper.