Locomotion Control of Quadruped Robots With Online Center of Mass Adaptation and Payload Identification

Ding, Chao; Zhou, Lelai; Li, Yibin; Rong, Xuewen

doi:10.1109/access.2020.3044933

Cited by 11 publications

(1 citation statement)

References 27 publications

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“…But the estimation accuracy of the payload parameters in these methods is unsatisfactory. In addition, some online payload estimation methods [17] have been proposed, such as the recursive least squares (RLS) methods [18][19][20], the Kalman filter methods [21,22], the adaptive algorithms [23], and the algebraic methods [24]. However, the identification accuracy of these online methods is usually worse than that of off-line methods.…”

Section: Introductionmentioning

confidence: 99%

An accurate identification method based on double weighting for inertial parameters of robot payloads

Fan

Fang

et al. 2022

Robotica

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The inertial parameters of payloads attached to the end effector of robots benefit to several robotics applications, such as the model-based control, the task optimization, and so on. These applications require the accurate estimation of the inertial parameters. In the existing payload estimation approaches, however, the data weighting technique, which can reduce the adverse effects of outliers and significantly improve the final results, has not been applied yet. In this article, an accurate identification method based on double weighting for inertial parameters of robot payloads is proposed. In order to obtain the weighting matrices, a modified dynamic parameter identification method with two loops is firstly proposed. Then, based on the identified results of dynamic parameters, a payload identification model based on double weighting is constructed. In addition, the variations of both nonlinear friction parameters and linear friction parameters caused by the payload are considered in this model. Finally, experimental comparisons between our method and another four methods are conducted. The results confirm that our method shows the best performance, especially on improving the identification accuracy of mass and center of mass of the payload.

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