Accurate image capturing control of bottle caps based on iterative learning control and Kalman filtering

Wenju Zhou, Minrui Fei, Kang Li, Haikuan Wang, Haoliang Bai

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

This paper investigates camera control for capturing bottle cap target images in the fault-detection system of an industrial production line. The main purpose is to identify the targeted bottle caps accurately in real time from the images. This is achieved by combining iterative learning control and Kalman filtering to reduce the effect of various disturbances introduced into the detection system. A mathematical model, together with a physical simulation platform is established based on the actual production requirements, and the convergence properties of the model are analyzed. It is shown that the proposed method enables accurate real-time control of the camera, and further, the gain range of the learning rule is also obtained. The numerical simulation and experimental results confirm that the proposed method can not only reduce the effect of repeatable disturbances but also non-repeatable ones.
Original languageEnglish
Pages (from-to)465-477
Number of pages13
JournalTransactions of the Institute of Measurement and Control
Volume36
Issue number4
Early online date25 Oct 2013
DOIs
Publication statusPublished - Jun 2014

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