Recent decades have been witness to explosive leaps in manufacturing productivity. Advances in communication technology, computing speed, control theory, and sensing technology have been significant contributors toward the increased productivity and efficiency that industry has exhibited. The continued growth of technological equipment and engineering knowledge challenges engineers to fully utilize these advancements in more sophisticated and useful automation systems.
One such application involves enhancing bridge and gantry crane operation. These systems are used throughout the globe, and are critical aspects of industrial productivity. Consequently, improving the operational effectiveness of cranes can be extremely valuable.
Effective control of cranes can be largely attributed to two distinct, but related aspects crane manipulation: 1) the expertise of operators, which are responsible for issuing commands to the structures, and 2) the dynamic properties of cranes, which influence how the structures respond to issued commands. Accordingly, the operational efficiency of cranes can be influenced by changing both the way that operators issue commands to cranes, and also how the crane responds to issued commands.
This thesis is concerned with dynamic control theory of flexible machines, and human/machine interaction, especially as these areas relate to industrial crane control. In the area of dynamic control, this thesis investigates control strategies that are specifically suited for use on systems that possess common actuator nonlinearities, like saturation, rate limiting, dead-zone, backlash, and finite-state actuation. In the area of human/machine interaction, this thesis investigates the effects of different crane interface devices on the operational efficiency of cranes.
Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/24773 |
Date | 08 July 2008 |
Creators | Sorensen, Khalid Lief |
Publisher | Georgia Institute of Technology |
Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
Detected Language | English |
Type | Dissertation |
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