On Improving Transient Behavior and Steady-State Performance of Model-free Iterative Learning Control

被引:2
作者
Zhang, Geng-Hao [1 ]
Chen, Cheng-Wei [1 ]
机构
[1] Natl Taiwan Univ, Dept Elect Engn, Taipei, Taiwan
来源
IFAC PAPERSONLINE | 2020年 / 53卷 / 02期
关键词
Iterative learning control; Model-free; Convergence analysis; TIME;
D O I
10.1016/j.ifacol.2020.12.1914
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A novel model-free iterative learning control algorithm is proposed in this paper to improve both the robustness against output disturbances and the tracking performance in steady-state. For model-free ILC, several methods have been investigated, such as the time-reversal error filtering, the Model-Free Inversion-based Iterative Control (MFIIC), and the Non-Linear Inversion-based Iterative Control (NLIIC). However, the time-reversal error filtering has a conservative learning rate. Other two methods, although with much faster error convergence, have either a high noise sensitivity or a non-optimized steady-state. To improve the performance and robustness of model-free ILC, we apply the time-reversal based ILC and recursively accelerate its error convergence using the online identified learning filter. The effectiveness of the proposed algorithm has been validated by a numerical simulation. The proposed approach not only improves the transient response of the MFIIC, but achieves lower tracking error in steady-state compared to that of the NLIIC. Copyright (C) 2020 The Authors.
引用
收藏
页码:1433 / 1438
页数:6
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