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考虑非线性摩擦模型的机器人动力学参数辨识

席万强 陈柏 丁力 吴洪涛 谢本华

农业机械学报2017,Vol.48Issue(2):393-399,7.
农业机械学报2017,Vol.48Issue(2):393-399,7.DOI:10.6041/j.issn.1000-1298.2017.02.053

考虑非线性摩擦模型的机器人动力学参数辨识

Dynamic Parameter Identification for Robot Manipulators with Nonlinear Friction Model

席万强 1陈柏 1丁力 1吴洪涛 1谢本华1

作者信息

  • 1. 南京航空航天大学机电学院,南京210016
  • 折叠

摘要

Abstract

Aiming at the dynamical parameter identification for robot manipulator,the artificial bee colony algorithm for identification was proposed.Considering the friction characteristics that the friction model was unable to reappear the behavior of complex dynamic friction at low speeds,the nonlinear robot model contained the nonlinear friction model was deduced by introducing the nonlinear Daemi-Heimann model.Then,the five order Fourier series was designed as exciting trajectory to collect experimental data,which satisfied velocity and acceleration boundary conditions.With the artificial bee colony algorithm,the colony bee was employed as search unit to identify unknown parameters which included 15 minimum inertia parameters and 12 friction parameters in the model through exchanging the information and retaining the superior individual.Finally,the established model was validated and analyzed,and all the results demonstrated that the proposed identification algorithm can accurately identify the dynamical parameters,and it also had high-speed convergence,strong search capability and can achieve the accurate prediction of robot driving torque.Compared with the linear dynamic model,the established nonlinear dynamical model can effectively improve the condition of sudden change about friction torque at the moment of joints commutation and can well reflect the dynamical characteristics of robot.

关键词

机器人/参数辨识/非线性摩擦模型/人工蜂群算法

Key words

robot/parameter identification/nonlinear friction model/artificial bee colony algorithm

分类

信息技术与安全科学

引用本文复制引用

席万强,陈柏,丁力,吴洪涛,谢本华..考虑非线性摩擦模型的机器人动力学参数辨识[J].农业机械学报,2017,48(2):393-399,7.

基金项目

国家自然科学基金项目(51575256、51375230) (51575256、51375230)

农业机械学报

OA北大核心CSCDCSTPCD

1000-1298

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