NDLI: Variable gain control of elastic crane using vision sensor data

Content Provider	IEEE Xplore Digital Library
Author	Hussein, M.T. Soffker, D.
Copyright Year	2012
Description	Author affiliation: Chair of Dynamics and Control, University of Duisburg-Essen, Duisburg, Germany (Hussein, M.T.; Soffker, D.)
Abstract	The estimation of state variables plays a significant role in the control of ship-mounted elastic cranes. Elastic-ship mounted crane can show very complex dynamic vibrations during their operations. A vision sensor (i.e. camera) realizing a contactless measurement sensor can be used to measure the deformations. Unfortunately effects like limited accuracy and time delay occur, which are the main inherent problems of vision sensors. The effects and related compensation approaches are studied in this work. The main goal of the work is to develop an approach to combine suitable measurement devices easy to realize with improved reliability. The task to be solved is to combine the estimations of the variable gain observer (based on vision measurements) with those of the variable gain observer (based on potentiometers). Realizing a multi-model approach the variable gain controller uses the estimated states and the roll angle to generate the required damping to control the system. Simulation results show that the variable gain observers can estimate the states and the unknown disturbance acting on the payload very well and the variable gain controller can reduce effectively the payload pendulations.
Starting Page	1783
Ending Page	1788
File Size	191315
Page Count	6
File Format	PDF
ISBN	9781467318716
e-ISBN	9781467318723
e-ISBN	9781467318709
DOI	10.1109/ICARCV.2012.6485420
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Observers Cranes Mathematical model Robot sensing systems Cameras Delay effects Gain
Content Type	Text
Resource Type	Article

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