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Minimal linear combinations of the inertia parameters of a manipulator

机译:机械手惯性参数的最小线性组合

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This paper deals with the problem of identifying the inertia parameters of a manipulator. We begin by introducing the terminology of minimal linear combinations (MLC's) of the inertia parameters that are linearly independent of one another and determine the manipulator dynamics while keeping the number of linear combinations of the inertia parameters to a minimum. The problem is then to find an identification procedure for estimating the MLC's and to use the MLC's in the inverse dynamics for control. The recursive Newton-Euler formulation is rederived in terms of the MLC's. The resulting formulation is almost as efficient as the most efficient formulation in the literature. This formulation also provides a starting point from which to derive a recursive identification procedure. The identification procedure is simple and efficient, since it does not require symbolic closed-form equations and it has a recursive structure. The three themes concerning the dynamic modeling of a manipulator $the MLC's, the inverse dynamics in terms of the MLC's, and the identification procedure - are treated in sequence.
机译:本文涉及识别机械手的惯性参数的问题。我们首先介绍彼此线性独立的惯性参数的最小线性组合(MLC)的术语,并确定机械手动力学,同时将惯性参数的线性组合的数量保持最小。然后的问题是找到用于估计MLC的识别程序,并在逆动力学中使用MLC进行控制。递归牛顿-欧拉公式用MLC表示。所得制剂几乎与文献中最有效的制剂一样有效。这种表述还提供了一个出发点,从该出发点可以得出递归识别程序。识别过程简单高效,因为它不需要符号封闭形式的方程式,并且具有递归结构。依次处理了涉及机械手动态建模的三个主题(MLC,关于MLC的逆动力学以及识别过程)。

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