Iterative Learning Trajectory Tracking Control of Heavy-load Robot for Intelligent Casting Production Line

胡文洪; 王宪; 彭兆鑫; 舒赞辉

doi:10.13433/j.cnki.1003-8728.20230224

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Iterative Learning Trajectory Tracking Control of Heavy-load Robot for Intelligent Casting Production Line

更新时间：2026-04-23

- Iterative Learning Trajectory Tracking Control of Heavy-load Robot for Intelligent Casting Production Line
- Mechanical Science and Technology for Aerospace Engineering Vol. 44, Issue 4, Pages: 581-591(2025)
- 作者机构：
  
  1. 湖南科技大学机构机电工程学院,湖南,湘潭,411100
  2. 长沙长泰机器人有限公司,长沙,410000
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230224
  CLC：
- Published：2025
- 稿件说明：
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胡文洪, 王宪, 彭兆鑫, et al. Iterative Learning Trajectory Tracking Control of Heavy-load Robot for Intelligent Casting Production Line[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(4): 581-591.
DOI：

胡文洪, 王宪, 彭兆鑫, et al. Iterative Learning Trajectory Tracking Control of Heavy-load Robot for Intelligent Casting Production Line[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(4): 581-591. DOI： 10.13433/j.cnki.1003-8728.20230224.

摘要

围绕混线生产的智能化铸造生产线对重载机器人末端执行器高精度控制的需求

提出了一种自适应迭代学习轨迹跟踪控制的方法。首先

建立了考虑末端负载质量影响的重载机器人动力学模型

分析了重载机器人砂芯搬运时的力学变化特性; 然后

在传统迭代学习控制律的基础上

提出了一种新的随负载质量改变自适应调整增益的迭代学习控制律

给出了控制参数的调整策略。仿真结果表明

本文提出的控制方法具有更快的迭代学习速度

机器人的最大距离误差较传统迭代学习控制方法低56.1%以上

负载的改变对控制精度的影响可以忽略不计。本文控制方法可以提高重载机器人迭代学习速度和轨迹跟踪精度。

Abstract

To meet the requirements of high precision control of heavy-load robot's end-effector in intelligent casting production line with mixed line production

an adaptive iterative learning trajectory tracking control method is proposed. Firstly

a dynamic model of a heavy-load robot is established by considering the influence of end-load mass

and the dynamic characteristics of the robot during the sand core transportation are analyzed. Then

based on the traditional iterative learning control law

a new iterative learning control law which adaptively adjusts the gain with the change of the mass is proposed

and the control parameters adjustment strategy is given. Finally

the simulation results show that the proposed control method has a faster iterative learning speed

the maximum distance error of the robot is more than 56.1% lower than that of the traditional iterative learning control method

and the influence of load change on the control accuracy can be ignored. The proposed control method can improve the iterative learning speed and trajectory tracking accuracy of the heavy-load robot.

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