.png){kind=icon}
.png){kind=icon}
Return
Iterative Learning Control for Path-Following Tasks With Performance Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/TCST.2021.3062223.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The classical problem setup of iterative learning control (ILC) is to enforce tracking of a reference profile specified at all time points in the fixed task duration. The removal of the time specification releases significant design freedom in how the path is followed but has not been fully exploited in the literature. This article unlocks this extra design freedom by formulating the ILC task description to handle repeated path-following tasks, e.g., welding and laser cutting, which aim at following a given spatial path defined in the output space without any temporal information. The general ILC problem is reformulated for ILC design with the inclusion of an additional performance index, and the class of piecewise linear paths is characterized for the reformulated problem setup. A two-stage design framework is proposed to solve the characterized problem and yields a comprehensive algorithm based on an ILC update and a gradient projection update. This algorithm is verified on a gantry robot experimental platform to demonstrate its practical efficacy and robustness against model uncertainty.
Keywords:
Iterative learning control
Hilbert space
Iterative learning control (ILC)
optimization
path following
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
4.8K
Citations:
1.7W