关键词: 多级流道式磁流变阻尼器, 双曲正切曲线, 正向运动学模型, 改进遗传算法, 参数辨识

Abstract:

The multi-stage flow channel magnetorheological damper (MFC-MRD) in the hydraulic actuator can effectively improve the underdamping characteristics of the hydraulic valve-controlled cylinder system and improve the stability of the system. It has potential application value in hydraulic equipment such as legged robots and excavators. However, the high power density design of MFC-MRD will inevitably increase the effective length of the damping channel, thereby increasing the damping force of the system, resulting in the existing dynamic model can not accurately describe the nonlinear hysteresis characteristics of MFC-MRD. In order to improve its nonlinear hysteresis characteristics, based on the mechanical performance test, and by analyzing the multi-stage flow channel structure and nonlinear hysteresis curve characteristics, the hyperbolic tangent curve is segmented and reorganized, and then an improved hyperbolic tangent model conforming to the hysteresis characteristics is proposed. Secondly, in the process of parameter identification, in order to avoid the problem of model parameters falling into local optimum and ' premature ', the selection operator of genetic algorithm is improved, and then a three-level stepwise approximation selection operator is proposed to improve the identification accuracy of model parameters. According to the mechanical experimental data, the relationship between model parameters and current is accurately obtained. Finally, through the model comparison, it can be seen that compared with the Bouc-Wen model and the data-driven model, the accuracy of the improved hyperbolic tangent model established in this paper is increased by up to 75%. It can accurately describe the nonlinear hysteresis characteristics of MFC-MRD, and verify the superiority and accuracy of the improved model.

Key words: multi-stage flow channel magnetorheological damper, hyperbolic tangent curve, forward kinematics model, improved genetic algorithm, parameter identification