2012
DOI: 10.1177/1077546312461371
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A hybrid command-shaper for double-pendulum overhead cranes

Abstract: A crane is generally modeled as a simple pendulum with a point mass attached to the end of a massless rigid link. Numerous control systems have been developed to reduce payload oscillations in order to improve safety and positioning accuracy of crane operations. However, large-size payloads may transform the crane model from a simple-pendulum system to a double-pendulum system. Control systems that consider only one mode of oscillations of a double pendulum may excite large oscillations in the other mode. In m… Show more

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Cited by 68 publications
(45 citation statements)
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“…Masoud et al presented a hybrid command-shaper, which combined a primary double-step command-shaper with a virtual feedback controller. Simulated and experimental results verified the effectiveness on a model of a double-pendulum overhead crane [13]. Singhose and Vaughan designed a multi-mode input shaper to reduce double-pendulum oscillations for the multi-hoist cranes [17][18].…”
Section: Introductionmentioning
confidence: 69%
See 2 more Smart Citations
“…Masoud et al presented a hybrid command-shaper, which combined a primary double-step command-shaper with a virtual feedback controller. Simulated and experimental results verified the effectiveness on a model of a double-pendulum overhead crane [13]. Singhose and Vaughan designed a multi-mode input shaper to reduce double-pendulum oscillations for the multi-hoist cranes [17][18].…”
Section: Introductionmentioning
confidence: 69%
“…While limiting (10) and (11) to zero and from (12)(13)(14), the smoothed impulse command is described by …”
Section: Command Smoothing For Vibration Suppressionmentioning
confidence: 99%
See 1 more Smart Citation
“…Some researchers have presented several methods to attack the issue, i.e., decoupling control [14], passivity-based control [15], wave-based control [16], hierarchical sliding mode control [17,18], input shaping control [12,13,[19][20][21][22][23], neuralnetwork-based intelligent control [24], e ct. The methods can roughly be divided into two categories according to the type of control structure, i.e., feedback control [14][15][16][17][18]24] or feedforward control [12,13,[19][20][21][22][23].…”
Section: Introductionmentioning
confidence: 99%
“…The methods can roughly be divided into two categories according to the type of control structure, i.e., feedback control [14][15][16][17][18]24] or feedforward control [12,13,[19][20][21][22][23]. Any method has both merits and drawbacks.…”
Section: Introductionmentioning
confidence: 99%