Operating cranes is challenging because payloads can experience large and dangerous oscillations. The oscillations are induced by both intentional motions commanded by the human operator and by external disturbances. Although significant progress has been achieved by using command shaping to reduce operator-induced vibration, less success has been achieved on reducing oscillations induced by external disturbances, such as wind. The disturbance-rejection task is more challenging because it requires accurate sensing of the crane payload. This study presents a combined command shaping and feedback control architecture. The input shaper eliminates the payload oscillation caused by human-operator commands, and the feedback controller reduces the effect of wind gusts. Simulations of a large range of motions are used to analyse the dynamic behaviour of boom cranes using the proposed controller. Experimental results obtained from a small-scale boom crane validate the simulated dynamic behaviour and the effectiveness of the controller.
Boom cranes are used for numerous material-handling and manufacturing processes in factories, shipyards, and construction sites. All cranes lift their payloads by hoisting them up using overhead suspension cables. Boom cranes move payloads by slewing their base about a vertical axis, luffing their boom in and out from the base, and changing the length of the suspension cable. These motions induce payload oscillation. The problem of payload oscillation becomes more challenging when the payload exhibits double-pendulum dynamics that produce two varying frequencies of oscillation. This paper studies the swing dynamics of such cranes. It also applies input shaping to reduce the two-mode oscillatory dynamics. Experiments confirm several of the interesting dynamic effects.
Cranes are the primary heavy lifters for a wide variety of industries. However, all cranes share the same important limitation on efficiency: payload oscillation. Given the significance of cranes, it is not surprising that a large amount of research has been dedicated to eliminating this oscillation. Much of this research has been directed toward feedback control methods. Another portion of the work has focused primarily on command-shaping methods. This paper explores the problems with using feedback for crane control, which include the difficulty in sensing the crane payload, widely varying system dynamics, and human-operator compatibility. In light of these problems, input shaping is shown to be a favorable solution.
Cranes are used in manufacturing facilities, throughout nuclear sites, and in many other applications for various heavy-lifting purposes. Unfortunately, the flexible nature of cranes makes fast and precise motion of the payload challenging and dangerous. Certain applications require the coordinated movement of multiple cranes. Such tasks dramatically increase the complexity of the crane operation. This paper studies the dynamic behavior of a dual-hoist bridge crane. Simulations and experiments are used to develop an understanding of the dynamic response of the system. Various inputs and system configurations are analyzed and important response characteristics are highlighted.
Cranes are vital to many manufacturing and material-handling processes. However, their physical structure leads to flexible dynamic effects that limit their usefulness. Large payload swings induced by either intentional crane motions or external disturbances decrease positioning accuracy and can create hazardous situations. Boom cranes are one of the most dynamically complicated types of cranes. Boom cranes cannot transfer the payload in a straight line by actuating only one axis of motion because they have rotational joints. This paper presents a nonlinear model of a boom crane. A large range of possible motions is analyzed to investigate the dynamic behavior of the crane when it responds to operator commands. A command-shaping control technique is implemented, and its effectiveness on this nonlinear machine is analyzed. Experimental results verify key theoretical predictions.
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