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Fuzzy iterative learning control for nonlinear parabolic distributed parameter systems

delete2025-09-22
delete0
PRE
AI
X
Xisheng Dai *
Y
Yanxue Wang
S
Senping Tian
Y
YangQuan Chen
Z
Zhijia Zhao
DOI：10.1016/j.fss.2025.109603delete
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Abstract

Abstract

En 中文
This study investigates fuzzy iterative learning control (ILC) for a class of nonlinear parabolic distributed parameter system (DPS). A Takagi-Sugeno (T-S) fuzzy DPS model with parameter uncertainty is proposed to approximate the nonlinear DPS. Based on this model, a fuzzy p-type ILC algorithm related to membership function is designed, which can adjust learning gain according to the system output error. Moreover, the fuzzy learning gain of this algorithm can be obtained by solving linear matrix inequality (LMI). By constructing a Lyapunov function, the system's tracking error in terms of L2-norm is proven to converge to zero, while input error is proved monotonically convergent as well. Finally, the effectiveness of the algorithm is verified by two numerical simulations.

Journal

Fuzzy Sets and Systems cover
Fuzzy Sets and Systems
IF:
2.7
Papers:
7.6K
Citations:
1.5W

Organization

G
Guangxi University of Science and Technology
Scholars:
902
Papers: 306
Citations: 2
U
university of california
Scholars:
1.9W
Papers: 8.0K
Citations: 10
G
Guangzhou University
Scholars:
1.7W
Papers: 1.3W
Citations: 1.8W
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