Simulation analysis of random initial error with iterative learning control method for Robot Arms

被引：0

作者：

Lu, Zhengjie ^{[1
]}

Chen, Mengji ^{[1
]}

Zhang, Yinjun ^{[1
,2
]}

机构：

[1] School ofMechanical and Electrical Engineering, Hechi University, Yizhou, Cuangxi, China

[2] School of mechanical and electrical engineering, Cuangxi Science and Technology Normal University, Laibin, China

来源：

Engineering Intelligent Systems | 2019年 / 27卷 / 04期

关键词：

Random errors - Two term control systems - Iterative methods - Learning algorithms;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In this paper, Iterative Learning Control (ILC) is used as the core algorithm. By improving ILC algorithm, a control algorithm suitable for trajectory tracking of industrial robots is proposed. Without resetting the initial conditions, an iterative learning control method is designed to accelerate the suppression of random initial state errors. A modified initial state error interval is defined, which decreases with the ngmber of iterations. Combining with the iterative learning control algorithm, the industrial robot can track the trajectory without resetting the initial conditions, and the tracking error converges to zero asymptotically. In terms of A norm, the convergence of the iterative learning control algorithm is proved. The simulation experiment results of the iterative learning control algorithm for accelerating the suppression of random initial state error are given, and compared with the simulation experiment results of the iterative learning control method without acceleration suppression rando|n initial state error. The results show that the proposed condition-free acceleration is effective. The iterative learning control method for suppressing the random initial state error has a good inhibitory effect on the random initial error of industrial robots. © 2019 CRL Publishing Ltd

引用

页码：201 / 211