• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.450-453
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• 1997

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. The result of simulations shows that the crane is just controlled for a very large swing angle of 1 radian within nearly one cycle.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.128-135
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• 2005

Overhead crane systems consist of trolley, girder, rope, objects, trolley motor, girder motor, and hoist motor. The dynamic system of these systems becomes a nonlinear state equations. These equations are obtained by the nonlinear equations of motion which are derived from transfer functions of driving motors and equations of motion for objects. From these state equations, Lyapunov functions of overhead crane systems are derived from integral method. These functions secure stability of autonomous overhead crane systems. Also constraint equations of driving motors of trolley, girder, and hoist are derived from these functions. From the results of computer simulation, it is founded that overhead crane systems is secure.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1079-1082
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• 1996

Automatic control systems for warehouse composed of unmanned crane system and vision system. Unmanned crane system is introduced to reject oscillations of a load suspended from a trolley at a moment of its arrival at its target position. And vision system is applied to find out the coordinates of coils on trucks using image processing.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1729-1734
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• 2005

The use of gantry crane systems for transporting payload is very common in industrial application. However, moving the payload using the crane is not an easy task especially when strict specifications on the swing angle and on the transfer time need to be satisfied. To overcome this problem, this paper describes development of an intelligent gantry crane system based on the mechatronic design. A lab-scale gantry crane is designed and then its intelligent controllers are developed. Fuzzy logic controllers are adopted, designed and implemented for controlling payload position as well as the swing angle of the gantry crane. The performance of the intelligent gantry crane system is evaluated on a hardware-in-the-loop simulation (HILS) environment. Moreover robustness of the proposed system is also evaluated. The result shows that the intelligent gantry crane system designed based on the mechatronic design approach has better performance compared with the automatic gantry crane system controlled by classical PID controllers. Moreover simulation result shows that the intelligent gantry crane system is more robust to parameter variation than the automatic gantry crane system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1250-1253
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• 1993

The fuzzy control theory is applied to control a container crane, which is a very complicated system and controled manually by experts. As reference velocities of trolley and hoist of the container crane, we use those decided by experts, and express them by fuzzy model. We control the crane to follow the reference velocities by using fuzzy controllers. The fuzzy controllers are designed on the container crane. We made a model container crane and applied the suggested method to it

• Journal of the Korea Safety Management & Science
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• v.24 no.1
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• pp.91-98
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• 2022

Since cranes are a kind of complex human-machine systems, it is almost impossible to completely secure safety with current technologies. Therefore, managerial interventions to prevent human errors are needed for safely operating a crane. The Occupational Safety and Health law states that cabin-type crane operators should have crane drivers' licence and crane-related operators (e.g., pendent-type crane operators, slinging workers) should take a special safety training. However, statistics on industrial accidents showed that fatalities due to crane accidents (185 accidents occurred during 2013~2017) were the highest among hazardous machinery and equipment. To effectively control the crane-related accidents, voices of crane workers need to be analyzed to investigate the current status. This study surveyed perceived causes of crane accidents and status of special safety training for crane workers of 387. The survey revealed that 24.3% of the respondents experienced crane accidents and 31.4% eye-witnessed crane accidents. 79% of the respondents pointed human errors such as improper crane operation and improper slinging as the first cause. Lastly, only 16.7% of the respondents took a professional special safety training; but the rest took lecture-based or incomplete education. The findings of the present study can be applied to improve crane-related policies and special safety training systems.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.283-283
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• 2000

In this paper, we deals with a decentralized control scheme with input compensation form for gantry crane systems. By considering the gantry crane's characteristics, the system is decentralized into two subsystems such as the travelling and sway subsystem, and the hoisting subsystem. For decentralizing the system, a simple algorithm is proposed using observability canonical form. The decentralized subsystems include unknown input which coupled with other subsystems and actuator failures. These unknown input and actuator failures are estimated by using PI observation techniques and those estimated values are used to construct an input compensation form. Lastly, the proposed decentralized control scheme far the gantry crane systems is verified by crane simulation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• Journal of Mechanical Science and Technology
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• v.20 no.5
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• pp.591-601
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• 2006

In this paper, we present a new control methodology for perturbed crane systems. Nonlinear crane systems are transformed to linear models by feedback linearization. An inverse dynamic equation is applied to compute the system PD control force. The PD control parameters are selected based on a nominal model and are therefore suboptimal for a perturbed system. To achieve the desired performance despite model perturbations, we construct a neural network auxiliary controller to compensate for modeling errors and disturbances. The overall control input is the sum of the nominal PD control and the neural auxiliary control. The neural network is iteratively trained with a perturbed system until acceptable performance is attained. We apply the proposed control scheme to 2- and 3-degree-of-freedom (D.O.F.) crane systems, with known bounds on the payload mass. The effectiveness of the control approach is numerically demonstrated through computer simulation experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.281-287
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• 2001

In this paper, we deal with a decentralized control scheme with input compensation form for gantry crane systems. By considering the gantry cranes characteristics, the system is decentralized into two subsystems, the travelling and swaying subsystem and the hoisting subsystem. For decentralizing the system, a simple algorithm is proposed using the observability canonical form. The decentralized subsystems include unknown inputs that one coupled with other subsystems and actuator failures. These unknown input and actuator failures are estimated by using PI observation techniques. And those estimated values are used to construct an input compensation form. Finally, the proposed decentralized control scheme for the gantry crane systems is verified by crane simulation.