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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (7): 1340-1348    DOI: 10.3785/j.issn.1008-973X.2019.07.013
Automatic Technology, Computer Technology     
Research and application of iterative learning control with knowledge inheritance
Chen-yang PU(),Zuo-jun LIU*(),Shuang PANG,Yan ZHANG
School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300130, China
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Abstract  

A new iterative learning control (ILC) strategy based on knowledge inheritance was proposed for a class of multi-dimensional trajectory with homogenous features. A kind of industrial robot system was taken as the control object throughout the tracking process. The homogeneous trajectory group (HTG) which was characterized by a gradual change in amplitude and the initial trajectory in HTG were respectively introduced. Then ILC scheme was utilized to track the initial trajectory in HTG. The effective knowledge could be obtained through ILC scheme from the initial trajectory. The knowledge was inherited to the next new trajectory in HTG for the first iteration. Gain transformation and offset transformation were applied according to the association of adjacent trajectories in HTG in order to effectively make the knowledge be inherited. Then the ILC with knowledge inheritance could make the industrial robot system track the new trajectory in fewer iterations. The overall learning times of tracking the HTG can be reduced and the tracking efficiency can be significantly improved compared with the traditional ILC. The theoretical analysis was presented to prove the convergence of the ILC based on knowledge inheritance, and the simulation results showed the advantage of the proposed control strategy.

Key words： iterative learning control (ILC)      industrial robot      homogeneous trajectory group (HTG)      initial trajectory      knowledge inheritance      tracking efficiency     
Received: 23 May 2018      Published: 25 June 2019
CLC:  TP 18  
Corresponding Authors: Zuo-jun LIU     E-mail: bjhync@126.com;liuzuojun@hebut.edu.cn
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Chen-yang PU
Zuo-jun LIU
Shuang PANG
Yan ZHANG
Cite this article:

Chen-yang PU,Zuo-jun LIU,Shuang PANG,Yan ZHANG. Research and application of iterative learning control with knowledge inheritance. Journal of ZheJiang University (Engineering Science), 2019, 53(7): 1340-1348.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.07.013     OR     http://www.zjujournals.com/eng/Y2019/V53/I7/1340


知识继承型迭代学习控制的研究与应用

针对一类具有同质特征的多维轨迹群，提出基于知识继承的迭代学习控制（ILC）策略. 该策略以一类工业机器人系统为控制对象，在跟踪具有渐变幅值的同质轨迹群（HTG）时，应用迭代学习控制方法，从起始源轨迹中获得基准控制知识. 将基准控制知识预设为下一新轨迹迭代学习的首次运行知识. 通过增益变换和偏移变换实现迭代学习控制的知识继承，使得该类工业机器人系统加快对新轨迹的学习速度，以此降低跟踪同质轨迹群的整体学习次数，实现跟踪效率的较大提升. 理论分析和仿真结果证明了所提控制策略的优越性.


关键词： 迭代学习控制（ILC）,  工业机器人,  同质轨迹群（HTG）,  源轨迹,  知识继承,  跟踪效率 
Fig.1 Spatial surface with homogeneous features
Fig.2 Industrial robot system for processing homogeneous trajectory group
Fig.3 Homogeneous trajectory group via spatial surface discreting
Fig.4 Tracking process of initial trajectory via ILC
Fig.5 Maximum error convergence process of initial trajectory along iterative axis
Fig.6 Tracking processes of HTG via ILC based on knowledge inheritance
Fig.7 Error convergence process of HTG via ILC based on knowledge inheritance
Fig.8 Comparison in HTG tracking of two different methods
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