Technical Briefs

Dynamics and Control of Bridge Cranes Transporting Distributed-Mass Payloads

[+] Author and Article Information
Raymond Manning, Jeffrey Clement, Dooroo Kim

Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332

William Singhose

Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332singhose@gatech.edu

J. Dyn. Sys., Meas., Control 132(1), 014505 (Dec 18, 2009) (8 pages) doi:10.1115/1.4000657 History: Received July 25, 2008; Revised September 30, 2009; Published December 18, 2009; Online December 18, 2009

The large-amplitude and lightly-damped oscillation of crane payloads is detrimental to safe and efficient operation. The problem is further complicated when the payload creates a double-pendulum effect. Previous researches have shown that single-mode oscillations can be greatly reduced by properly shaping the inputs to the crane motors. This paper builds on previous developments by thoroughly describing the double-pendulum dynamic effects as a function of payload parameters and the crane configuration. Furthermore, an input-shaping control method is developed to suppress double-pendulum oscillations created by a payload with distributed-mass properties. Experiments performed on a 10-ton industrial bridge crane verify the effectiveness of the method. A critical aspect of the testing was human operator studies, wherein numerous operators utilized the input-shaping controller to perform manipulation tasks. The performance improvements provided by the input-shaping controller, as well as operator learning effects, are reported.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 1

Input shaping a pulse input

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Figure 2

Typical crane responses

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Figure 3

Distributed-payload crane

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Figure 4

Low-mode frequencies

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Figure 5

High-mode frequencies

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Figure 6

Amplitude contribution of ω2

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Figure 7

Industrial bridge crane at Georgia Tech

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Figure 8

Sensitivity curves for slender-beam shapers

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Figure 9

Response near target location

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Figure 10

Completion time moving around an obstacle

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Figure 11

Operator effort moving around an obstacle

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Figure 12

Picture of course for task 1

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Figure 13

Payload used in operator learning study

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Figure 14

Task 1 average completion times

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Figure 15

Task 1 average final positioning error

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Figure 16

Task 1 average button pushes

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Figure 17

Task 2 average completion times




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