Modeling and identification of hysteresis of marine damper considering shock environment based on evolutionary sparrow search algorithm

Abstract

The mechanical properties of marine dampers are usually highly nonlinear in order to adapt to dynamic and shock environments. The dampers are subjected to dynamic and shock tests and the hysteresis curves are found to be rate-dependent and asymmetric. To be able to describe the dynamic hysteresis and shock hysteresis, a rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model is proposed based on the generalized Prandtl-Ishlinskii (GPI) model, which is a hybrid of the GPI model and the radial basis function (RBF) neural network. Due to the large number of parameters in the model, thus aiming to improve the effect of parameter identification, an evolutionary sparrow search algorithm mixed with sparrow search algorithm and differential evolutionary algorithm is proposed. Compared with other algorithms, sparrow search algorithm is verified to have ideal optimization effect and convergence speed, and it is not easy to fall into the local optimum. Finally, the RDGPI model is experimentally verified, and the results show that the prediction error of the RDGPI model for dynamic hysteresis is within 0.06 kN, and the prediction error for shock hysteresis is within 0.8 kN, which is capable of describing the rate-dependent characteristics of the damper hysteresis. And it has a very good generalization ability, which makes it very suitable to be used in the modeling of the mechanical characteristics of the marine damper.