Parameter identification of a novel strain stiffening model for magnetorheological elastomer base isolator utilizing enhanced particle swarm optimization

This article presents a novel model to describe the nonlinear relationships between shear force and displacement/velocity in a magnetorheological elastomer base isolator. The proposed model, containing a strain stiffening element, is able to portray the distinct dynamic behaviors of magnetorheological elastomer base isolator. To identify the model parameters, an enhanced particle swarm optimization is used on force–displacement/velocity data sampled under different loading conditions. In this algorithm, a self-adaptive inertia weight replaces the general linear weight, enhancing the convergence rate of iteration process. Besides, the mutation operator in genetic algorithm is adopted for finding global optimum. Testing data of the device displacement, velocity and force from magnetorheological elastomer base isolator are utilized to validate the proposed model and corresponding parameter identification algorithm.