摘要
针对工业机器人在不带负载时的动力学参数辨识问题,提出了一种基于加权最小二乘法与人工蜂群算法(WLS-ABC)的辨识算法.首先计及关节摩擦特性,推导出机器人动力学模型的线性形式;接着设计五阶傅里叶级数作为激励轨迹,采集辨识实验数据;然后根据文中辨识算法,采用加权最小二乘法得到待辨识参数初始解,并以蜂群为搜索单位,通过群体之间的信息交流与优胜劣汰机制找到全局最优参数;最后对得到的模型进行验证与分析.实验结果表明,通过文中辨识算法得到的预测力矩与测量力矩有较高的匹配度,所建立的模型能够反映机器人的动力学特性.
Aiming at the kinetic parameter identification of industrial robots without loads,a novel hybrid algorithm,which combines weighted least square method with artificial bee colony algorithm（ WLS-ABC）,is proposed. Firstly,a linear dynamic model of the robot considering the friction characteristics of joints is deduced.Secondly,a five-order Fourier series is designed to be the exciting trajectory and experimental data are collected and identified. Then,WLS is employed to obtain the initial solution of the collected experimental data. Moreover,bee colony is used as a search unit to find global optimal parameters through exchanging the information and retaining the superior individual. Finally,the established model is validated and analyzed. Experimental results show that the predicted torques well match the measured ones,and that the proposed model well reflects the kinetic characteristics of robots.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2016年第5期90-95,共6页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(51375230)
江苏省科技支撑计划重点项目(BE2013003-1
BE2013010-2)~~
关键词
工业机器人
参数辨识
加权最小二乘法
人工蜂群算法
industrial robots
parameter identification
weighted least square method
artificial bee colony algorithm
