Modeling of Hysteresis Effect of SMA using Neuro Fuzzy Inference System

Abstract

Hysteresis is the dependence of a physical property, not only on the present controlled parameters, but also on the path travelled. Although Shape Memory Alloys (SMAs) exhibit a myriad of nonlinearities, SMAs show two major types of nonlinear hysteresis. During cyclic loading of the SMAs, it is observed that one type of hysteretic behavior depends on the rate of heating the SMAs, whilst the variation of maximum temperature of SMA in each cycle results in the other hysteretic behavior. This hysteretic behavior gives rise to major and minor nonlinear loops of SMAs. The present work analyzes the nonlinearities of hysteretic envelopes, given the different maximum temperature reached for each hysteretic cycle on the strain of the SMA. This work then models this behavior using Adaptive Neuro Fuzzy Inference System (ANFIS) and compares it to experimental results. The nonlinear learning and adaptation of ANFIS architecture makes it suitable to model the temperature path hysteresis of SMAs