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A Smoothed GMS friction model suited for gradient-based friction state estimation

机译:适用于基于梯度的摩擦状态估计的平滑GMS摩擦模型

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The multi-state Generalized Maxwell-Slip (GMS) friction model is known to describe all essential friction characteristics in presliding and sliding motion. It is also known that due to its switching state conditions between presliding and sliding, gradient-based parameter and state estimation methods cannot be implemented efficiently. Efficient on-line state and parameter estimation is essential for model-based friction compensation in order to track changes of friction characteristics in time and space. This paper presents a Smoothed GMS (S-GMS) friction model with an analytic set of differential equations well suited for gradient-based estimation techniques. Friction state estimation is implemented with an Extended Kalman Filter (EKF) to validate the S-GMS model numerically and experimentally, and compare its behavior with the GMS model.
机译:众所周知,多状态广义Maxwell-Slip(GMS)摩擦模型描述了预滑动和滑动运动中的所有基本摩擦特性。还已知的是,由于其在预滑动和滑动之间的切换状态条件,所以不能有效地实现基于梯度的参数和状态估计方法。有效的在线状态和参数估计对于基于模型的摩擦补偿至关重要,以便跟踪时空摩擦特性的变化。本文介绍了一种平滑的GMS(S-GMS)摩擦模型,该模型具有一组非常适合基于梯度的估算技术的微分方程分析。摩擦状态估计通过扩展卡尔曼滤波器(EKF)进行,以数值和实验方式验证S-GMS模型,并将其行为与GMS模型进行比较。

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