In order to identify the nonlinear characteristics of the magnetorheological (MR) damper applied in multi-DOF vibration reduction platforms in the aerospace field in the modeling process, the least square support vector machine (LS-SVM) method is adopted, because LS-SVM can handle small-sample, high-dimensional characteristic problems. Firstly, the theory of the modeling method based on LS-SVM was illustrated including the genetic algorithm (GA) optimization method. Secondly, the characteristic curve of the MR damper was tested based on different conditions. Then, the current and historical input displacement, velocity, and current and the historical output are taken as the input of the LS-SVM model and the damping force of the current output is taken as the output of the model for model training. Meanwhile, the genetic algorithm is introduced to optimize the parameters of the LS-SVM model which affect the accuracy of the model, the penalty factor , and the kernel parameter after optimization. Finally, in order to verify the method adopted in the paper, the Simulink model was simulated in certain input conditions; by comparing the simulation and experimental values of this model, it is found that the maximum error is within 10 N and the average error is around 0.89 N, which is similar to the accuracy obtained in other works of literature, and the correctness of this model is verified.

中文翻译：

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基于LS-SVM的MR阻尼器建模与参数辨识

为了在建模过程中识别应用于航天领域多自由度减振平台的磁流变（MR）阻尼器的非线性特性，采用最小二乘支持向量机（LS-SVM）方法，因为LS-SVM可以处理小样本，高维特征问题。首先，阐述了基于最小二乘支持向量机的建模方法的理论，包括遗传算法的优化方法。其次，根据不同的条件测试了MR阻尼器的特性曲线。然后，将当前和历史输入的位移，速度，电流和历史输出作为LS-SVM模型的输入，并将当前输出的阻尼力作为模型训练的模型的输出。同时，，和内核参数经过优化。最后，为了验证本文采用的方法，在一定的输入条件下对Simulink模型进行了仿真。通过比较该模型的仿真和实验值，发现最大误差在10 N以内，平均误差在0.89 N左右，这与其他文献中获得的精度相似，并且该模型的正确性已验证。