Dynamic RC operator-based hysteresis model of MR dampers

Abstract

Nonlinear hysteretic mechanical behavior is a major obstacle to the efficient applications of vibration/shock and precision control systems with smart material-based actuators, such as magnetorheological (MR), magnetostrictive, piezoelectric and shape memory alloy actuators. In this paper, a dynamic resistor-capacitor (RC) operator (dRCO)-based hysteresis model(dRCOM) is proposed and thoroughly investigated for precise control of such actuators. The proposed dRCO links the RC operator (RCO) with the input rate, which enables the RCO the dynamic description performance. Employing the principle of graph neural network, the input data is preprocessed by the dRCO, and the neural network model is constructed for the parameter identification of the MR damper model. Experimental tests of the damping force characteristics of a commercial MR damper under different displacement excitations and applied currents are carried out. Correspondingly, mean square errors (MSEs) are compared between the tests and the outputs of the Bouc–Wen model, basic RCO-based hysteresis model (RCOM), and dRCOM. As a result, the dRCOM predicts MR damper more accurately than the other two. Furthermore, simulation and experimental tests of MR damper force tracking using different hysteresis models are also conducted to demonstrate the superiority of the dRCOM.