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LOAD SWAY PREVENTION AND CONTROL IN GANTRY CRANES USING FUZZY LOGIC

Carried out on 2012 in the University of Port Harcourt. By Kindson Munonye

Objectives of the Research

The Objectives of the research includes the following:

  • To design a fuzzy logic controller that automates the operation ofthe over-head crane and reduces the occurrence of laod sway to the minimum posible level
  • To compare the performance of position control between PID controller and fuzzy logic controller
  • Since the PID controller-based system is considered the most efficient so far, this work produces a system that eliminates the shortcomings of PID controller
  • To compare the performance of position control between different types of membership functions of fuzzy logic controller.

The research was carried out in 2013 as a requirement for a Post-Graduate diploma degree in Computer Science.

Load-Sway

Background
Cranes are machines used in various types of industrial operations expecially in the construction industry and projects. Load Sway or Swing has been a majoy challenge in Cranes operations due to being the a source of hazzard in the work environment when objects are to be moved from on location to another using a crane.

Conventional method of improving the competence of the crane operators have failed to provide the needed precision to handle this issue. That is why a Fuzzy-Logic approach is recommeded in this research.

The study proposes a fuzzy logic approach to load swing suppression .

In the first method described, a model-based sensor is not provided on the payload side. Here the swing motion of the payload is estimated based on the feedback from the dynamic model ofthe crane and the trolley position.

In the second method evaluated, a reference input modifiers is used to eliminae the need for swing angle sensor for automatics gantry crane making it possible to realize a sensorless anti-swing control.

In both models, the swing motionn is reduced by modifying the input parameter fed back into the fuzzy logic controller.

Implementation of this model on a lab-scale confirmed that that the proposed methods are effective. It was also confirmed through experiment that the proposed methods are robust to variations in the input parameters.


 
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