液压作动器中串联多级流道式磁流变阻尼器（MFC-MRD）可有效改善液压阀控缸系统的欠阻尼特性，提高系统的稳定性，该技术在足式机器人、挖掘机等装备中具有潜在的应用价值。但MFC-MRD的高功率密度设计必然会增加阻尼流道的有效长度，进而增大系统的阻尼力，导致现有动力学模型无法精确描述MFC-MRD的非线性滞回特征。为改善MFC-MRD的非线性滞回特性，该文基于力学性能试验，通过分析多级流道结构及非线性滞回曲线特征，对双曲正切曲线进行分割重组，进而提出了符合滞回特性的改进双曲正切模型；在参数辨识过程中，为避免模型参数陷入局部最优和“早熟”，对遗传算法的选择算子进行改进，进而提出了一种三级逐步逼近的选择算子，提高了模型参数的辨识精度，并依据力学实验数据，精确地获得了模型参数与电流的关系。模型对比结果表明，相较于Bouc-Wen模型和数据驱动式模型，该文建立的改进双曲正切模型的精度最高可提高75%，能准确地描述MFC-MRD存在的非线性滞回特性，具有较高的精确性。

The multi-stage flow channel magnetorheological damper (MFC-MRD) in the hydraulic actuator can effectively improve the underdamping characteristics of hydraulic valve-controlled cylinder system and improve the stability of the system. It possesses potential application value in such equipment such as legged robots and excavators. However, the high power density design of MFC-MRD may inevitably increase the effective length of the damping channel, thereby increasing the damping force of the system, which makes the existing dynamic model cannot accurately describe the nonlinear hysteresis characteristics of MFC-MRD. In order to improve the nonlinear hysteresis characteristics of MFC-MRD, on the basis of 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. In the process of parameter identification, to avoid model parameters falling into local optimum and “premature”, the selection operator of genetic algorithm is improved, and a three-level stepwise approximation selection operator is proposed to improve the identification accuracy of model parameters. Moreover, the relationship between model parameters and current is accurately obtained according to the mechanical experimental data. The comparison results of different models show that the improved hyperbolic tangent model established in this paper is of higher accuracy than the Bouc-Wen model and the data-driven model because it can accurately describe the nonlinear hysteresis characteristics of MFC-MRD, with an accuracy increase of up to 75%.