• 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.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.1
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• pp.9-16
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• 2010

This paper presents supervisor control methods at a matrix converter controlled overhead crane system based on a controller area network (CAN). Four induction motors are used to drive the gantry, trolley, and hoist at he crane and each motor is controlled by the matrix converter with direct torque control (DTC). Both the position control algorithm and the supervisor control system using CAN are introduced. Simulation and experimental results are carried out to verify the performance of position control at the matrix converter controlled crane system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.142-142
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• 2000

During the operation of crane system in container yard, it is necessary to control the crane trolley position so that the swing of the hanging container is minimized. Recently an automatic control system with high speed and rapid transportation is required. Therefore, we designed a controller to control the crane system with disturbances. In this paper, Ive present the neural network two degree of freedom PID controller to control the swing motion and trolley position. Then we executed the computer simulation to verify the performance of the proposed controller and compared the performance of the neural network PID controller with our proposed controller in terms of the rope swing and the precision of position control . Computer simulation results show that the proposed controller has better performances than neural network PID with disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.206-206
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• 2000

This paper is ai ed to handle quick work for all the workers and to improve the productivity by adding more effective content in Crane Monitoring System. The contributing proportion of the increase of port productivity is more increasing concerning not only the port industry, but also all the informations of container crane which is the representative equipment by the rapid increase of the volume of freight of port. The basic of rapid service is the improvement of the productivity, the information of operation as to the productivity of crane for the quick handling within yard and especially the informations of breakdown and to handle breakdown as soon as possible has a great enect on the increase of productivity.

• 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.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2788-2790
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• 2002

In this paper, we designed neural network predictive two degree of freedom PID controller to control sway of crane Crane's trolley arrive minimum oscillation of transfer body and establishment position in minimum time. When various establishment location and surrounding disturbance were approved based on mathematical modeling of crane, controller designed to become effective control location error and oscillation angle of two control variables that simultaneously can predictive control. We wish to develop automatic travel control system through anti-sway skill of crane.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.435-449
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• 2004

In this paper, a novel anti-sway control system that uses an inclinometer as a sway sensor is investigated. The inclinometer, when compared with a vision system, is very cheap, durable, and easy to maintain, while providing almost the same performance. A number of observers to estimate the angular velocity of the load and the trolley velocity are presented. A state feedback controller with an integrator is designed. After a time-scale analysis, a 1/4-size pilot crane of a rail-mounted quayside crane was constructed. The performance of the proposed control system was verified with a real rubber-tired gantry crane at a container terminal as well as with the constructed pilot crane. Experimental results are provided.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.03a
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• pp.219-227
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• 2002

In this paper, we designed neural network predictive PID controller to control sway happened in transfer of trolley for automatic travel control system. We include dynamic character of nonlinear system, and mathematical expression veny simple used neural network. When various establishment location and surrounding disturbance were approved based on mathematical modelling of crane, controller designed to become effective control location error and vibration angle of two control variables that simultaneously can predictive control. Neural network predictive PID controller produced parameter of PID controller using neural network self-tuner. Neural network self-tuner's input used crane's output and neural network predictive output. Neural network self-tuner using error back propagation algorithm. We analyzed control performance comparison through computer simulation when applied disturbance about sway of location and angle in transfer of crane. The results show that the proposed neural network predictive PID controller has better performances than general PID controller, neural network PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.709-716
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• 2005

As a way to build more efficient and intelligent container cranes for todays hub ports, communication networks are used to interconnect numerous sensors, actuators, controllers, and operator switches and consoles that are spatially distributed over a crane. Various signals such as sensor values and operator's commands are digitized and broadcast on the network instead of using separate wiring cables. This not only makes the design and manufacturing of a crane more efficient, but also easier implementation of intelligent control algorithms. This paper presents the performance evaluation of CAN(Controller Area Network), TTP(Time Triggered Protocol) and Byteflight that can be used for cranes. Through discrete event simulation, several important quantitative performance factors such as the probability of a transmission failure, average system delay (data latency) and maximum system delay have been evaluated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.397-402
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• 1998

PID controller has been used very widely in the industrial applications. But it is difficult tune the PID gains. In this paper, we present a design of optimal 2-DOF PID controller for control of container crane which has to control swing motion and trolley position. For tuning of the 2-DOF PID control gains, we used hybrid evolution program(EP). During operate the crane system in yard, the goal is transporting the load to a goal position as quick as possible without rope oscillation. The crane is generally operated by an expert operator, but recently an automatic control system with high accuracy and rapid transportation is required. However, we developed an optimal controller which has to control the crane system with disturbance.