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Research Papers

Load Position Estimation for Crane Anti-Sway Control Systems

[+] Author and Article Information
Ulf Schaper

Institute for System Dynamics,
University of Stuttgart,
Pfaffenwaldring 9,
Stuttgart D-70569, Germany
e-mail: schaper@isys.uni-stuttgart.de

Oliver Sawodny

Institute for System Dynamics,
University of Stuttgart,
Pfaffenwaldring 9,
Stuttgart D-70569, Germany
e-mail: sawodny@isys.uni-stuttgart.de

Michael Zeitz

Institute for System Dynamics,
University of Stuttgart,
Pfaffenwaldring 9,
Stuttgart D-70569, Germany
e-mail: zeitz@isys.uni-stuttgart.de

Klaus Schneider

Liebherr Werk Nenzing GmbH,
Postfach 10,
Nenzing A-6710, Austria
e-mail: k.schneider@liebherr.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 15, 2013; final manuscript received November 14, 2013; published online February 19, 2014. Assoc. Editor: Prashant Mehta.

J. Dyn. Sys., Meas., Control 136(3), 031013 (Feb 19, 2014) (7 pages) Paper No: DS-13-1200; doi: 10.1115/1.4026134 History: Received May 15, 2013; Revised November 14, 2013

A rising number of modern cranes are equipped with anti-sway control systems to facilitate crane operation, improve positioning accuracy, and increase turnover. Commonly, these industrial crane control systems require pendulum state information for feedback control. Therefore, a pendulum sway sensor (e.g., a rope-mounted gyroscope) and a signal processing algorithm are required. Such a signal processing algorithm needs to filter out disturbances from both the sensor and the crane, e.g., signal noise and string oscillations of a long rope. Typically, these signal processing algorithms require the knowledge of the acceleration of the rope suspension point. This acceleration signal is often estimated from drive models. When drive models are uncertain, the pendulum state estimation accuracy suffers from drive model inaccuracy. In this contribution, an improved estimation algorithm is presented which estimates the load position without relying on the rope suspension point acceleration. The developed Extended Kalman Filter is implemented on a Liebherr mobile harbor crane and its effectiveness is validated with multiple test rides and GPS load position reference measurements.

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Figures

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Fig. 3

Geometry, positions, variables, and forces of a rotary boom crane

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Fig. 2

Two-degree of freedom (2DOF) control with state observer implemented on Liebherr harbor mobile cranes

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Fig. 1

Liebherr crane LHM 550 during bulk cargo transloading

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Fig. 4

Gyroscope mounting on ropes of an LHM crane (the photo was taken while hook was on the ground)

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Fig. 5

Raw measurement data of a rope-mounted gyroscope during crane motion

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Fig. 6

Top view on crane ropes with no rope string oscillations (left) and heavy rope string oscillations (right)

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Fig. 7

Comparison of the presented load position estimate, a GPS reference measurement and the results from Ref. [16]. (a) Validation of load position estimates. (b) Validation of rope angle estimates.

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Fig. 8

Comparison of load position estimation during closed-loop operation of Liebherr crane LHM 400

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