.png){kind=icon}
.png){kind=icon}
Return
Nonrepetitive iterative learning control for 2-D uncertain systems with iteration-dependent uncertainties, random data dropouts and quantised measurements
.png){kind=icon}
PRE
AI
K
Y
DOI：10.1080/00207721.2025.2589463.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The existing iterative learning control (ILC) algorithms for 2-D discrete systems are concerned with the strictly repetitive case, and no ILC results for 2-D nonrepetitive uncertain systems (NUS) with communication network have been reported. This article first investigates the robust and convergent properties on a networked P-type ILC law for 2-D NUS, which is subject to iteration-dependent uncertainties (IDUs), random data dropouts and quantised measurements. By using the input signals at previous iteration and the 2-D reference trajectory, the random data dropouts occurred are compensated. A logarithmic quantiser is used to describe the quantised measurements. Consequently, it is theoretically proved that the ILC tracking error and the control input in the sense of mathematical expectation robustly converge to a bounded range, the bound of which relies continuously on the bounds of the IDUs, probability of the random data dropouts, and quantisation density. If these IDUs are progressively convergent along the iteration domain, a precise tracking result can be achieved with mathematical expectation sense. Additionally, an improved ILC law with compensation technique is applicable to the 2-D NUS with coupling input. An illustrative example on the practical thermal process is given to demonstrate the validity of the presented networked P-type ILC law.
Keywords:
2-D nonrepetitive uncertain systems (NUS)
iteration-dependent uncertainties (IDUs)
random data dropouts and quantised measurements
networked P-type ILC law
Journal
I
IF:
4.6
Papers:
1.0K
Citations:
7.3K
Organization
Cited Papers