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Filtering approaches for online identification of GMS friction model

机译:在线识别GMS摩擦模型的过滤方法

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This paper deals with an online identification of the Generalized Maxwell Slip (GMS) friction model for both presliding and sliding regime at the same time. This identification is based on robust adaptive observer without friction force measurement. To apply the observer, a new approach of calculating the filtered friction force from the measurable signals is introduced. Moreover, two approximations are proposed to get the friction model linear over the unknown parameters and an approach of suitable filtering is introduced to guarantee the continuity of the model. Simulation results are presented to prove the efficiency of the approach of identification.
机译:本文涉及同时预设和滑动制度的广义麦克斯韦平滑(GMS)摩擦模型的在线识别。该识别基于鲁棒的自适应观察者而没有摩擦力测量。为了应用观察者,介绍了从可测量信号计算过滤的摩擦力的新方法。此外,提出了两个近似以使摩擦模型线性在未知参数上线性,并引入了合适的滤波的方法以保证模型的连续性。提出了仿真结果以证明鉴定方法的效率。

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