Mem-models as building blocks for simulation and identification of hysteretic systems

In this study, mem-springs and mem-dashpots from a newly introduced family of mem-models are used as fundamental building blocks in hysteresis modeling. The usefulness of such assemblies of mem-models is investigated for both simulation and system identification. First, numerical simulations demonstrate the general capability of these models to describe strain ratcheting behaviors. Next, system identification is addressed by extending the concepts of mem-springs to include linear and nonlinear springs and those of mem-dashpots to include linear and nonlinear dashpots. A reconfigurable device made of steel and/or shape memory alloy wires and wire ropes provides a fitting test for the proposed mem-model-based family. A system identification procedure corroborated by physical insights is proposed and the results are validated using physics-based analysis. Multilayer feedforward neural networks are used for static nonlinear function approximation. The model class and system identification procedure proposed here are shown to extract similarities and dissimilarities among different configurations of the device by quantifying the spring and damping effects.