Parameter Identification of SCARA Robot Based on Random Weight Particle Swarm Optimization

doi:10.7652/xjtuxb202109003

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Parameter Identification of SCARA Robot Based on Random Weight Particle Swarm Optimization

更新时间：2025-07-09

- Parameter Identification of SCARA Robot Based on Random Weight Particle Swarm Optimization
- Vol. 55, Issue 9, Pages: 20-27(2021)
- 作者机构：
  
  兰州理工大学机电工程学院,兰州,730050
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202109003
  CLC： TP242
- Online First：10 September 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
Parameter Identification of SCARA Robot Based on Random Weight Particle Swarm Optimization[J]. 2021, 55(9): 20-27.
DOI：

Parameter Identification of SCARA Robot Based on Random Weight Particle Swarm Optimization[J]. 2021, 55(9): 20-27. DOI： 10.7652/xjtuxb202109003.

摘要

针对SCARA机器人在负载条件下末端动力学参数难以辨识的问题

在分析负载对各关节力矩影响的基础上

对SCARA机器人进行了结构简化

利用Lagrange法建立带负载机器人的动力学数学模型

确定了需要辨识的机器人末端动力学参数。在传统粒子群算法的基础上

提出一种随机权重粒子群算法对机器人动力学参数进行辨识

并编写了相应的程序。仿真辨识结果表明:随机权重粒子群算法的收敛速度与参数粒子搜索范围得到明显提升

辨识出的机器人力矩与实际输出力矩基本吻合

说明该算法对机器人动力学参数的辨识具有较高的精度; 与遗传算法、基本粒子群算法相比

随机权重粒子群算法辨识得到的适应度函数最优值最小

不易陷入局部最优

便于全局搜索

参数辨识精确更高。

Abstract

Aiming at the problem that it is difficult to identify the dynamic parameters of the SCARA robot end under the load condition

based on the analysis of load effect on the torque of each joint

the structure of SCARA robot was simplified. The Lagrange method was used to establish the dynamic mathematical model of the robot with load

and the dynamic parameters of the robot end that need to be identified were determined. On the basis of the traditional particle swarm optimization(PSO)

a random weight PSO was proposed to identify the dynamic parameters of the robot

and its corresponding program was given. The simulation results show that the random weight PSO algorithm can significantly improve the convergence rate and extend the search range of parameter particles; the identified robot torque is basically consistent with the actual output torque

which indicates that the algorithm has higher accuracy. Compared with the genetic algorithm and the basic PSO algorithm

the fitness function optimal value identified by the proposed random weight PSO algorithm is the smallest

and the proposed algorithm is convenient for global search and not easy to fall into the local optimization

and the parameter identification is more accurate.

关键词

Keywords

references

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