Non-parametric modeling of magnetorheological damper based on MEA-BP neural network

Abstract

The process of parametric modeling of magnetorheological (MR) damper presents difficulties such as numerous parameters, complicated and time-consuming identification procedures, and difficulty in establishing the inverse model. In order to solve these problems, this paper adopts the mind evolution algorithm backpropagation (MEA-BP) neural network to successfully establish the forward model and the inverse model for prediction of electric current. These models can accurately capture the nonlinear hysteresis characteristics of MR damper. First, the mechanical properties of the MR damper were tested, and then the BP neural network structure of the forward and inverse models was determined according to its working principle. Aiming at the problems of traditional BP neural network which is easy to mature prematurely and converge slowly, the initial weights and thresholds of BP neural network were optimized by using MEA. Utilizing the Mechanical properties test results of the MR damper, the MEA-BP neural network was used to identify the forward and inverse non-parametric models of the MR damper. Compared with the traditional BP neural network model, the forward model and inverse model of the MR damper, based on the MEA-BP neural network, showed an improvement in prediction accuracy by 33.96% and 26.32%, respectively. The forward model based on the MEA-BP neural network demonstrated a 16.67% improvement in accuracy compared to the genetic algorithm (GA) BP neural network. The non-parametric model, developed using the MEA-BP neural network, more accurately captures the dynamic behavior of the MR damper. This advancement provides a robust foundation for research into the dynamics and semi-active control of MR adaptive damping systems.