Dynamic identification of robots with power model

Abstract

This paper presents a new approach to identify the minimum dynamic parameters of robots using least squares techniques (LS) and a power model. Theoretical analysis is carried out from a filtering point of view and clearly shows the superiority of the power model over the energy one and over the dynamic identification model which has been used to carry out a classical ordinary LS estimation and a new weighted LS estimation. These results are checked from comparing experimental identification of the dynamic parameters of a planar SCARA prototype robot.