• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 1998.10a
• /
• pp.153-158
• /
• 1998

In this paper, we present a design of optimal 2-DOF PID controller for control of gantry crane which has to control swing motion and trolley position. For tuning the parameter of 2-DOF PID controller, we used evolution strategy(ES). 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 speed and rapid transportation is required. However, we developed an optimal controller which has to control the crane system with disturbance.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.112-117
• /
• 1997

This paper presents a GA(Genetic Algorithms)-Optical control strategy for the control of the swing motion and the transverse position of the overhead crane. The overhead crane system is defined uncertain due to unknown system parameters such as payload and trolly mass. To control the overhead crane. the GA-Optimal control scheme is suggested. which transfers a trolly to a desired place as fast as possible and minimizes the swing of the payload during the transfer. The genetic algorithms are applied to fine digital optimal feedback gains. A computer simulation demonstrate the performance of the proposed the GA-digital optimal controller for the overhead crane.

• Journal of Power System Engineering
• /
• v.12 no.6
• /
• pp.36-41
• /
• 2008

A crane is very important mechanical systems in industrial applications to move huge objects. Especially, in marine port terminals, it is used to place container boxes at desired position within given operating time. However, such system is faced with environmental disturbance such as wind from the sea, thus crane control system is required to cope with this nature. This paper proposed robust and adaptive control algorithm of a complicated 3D crane against the environmental disturbance. We simplify a mathematical design procedure to derive our control algorithm. We conduct real-time experiment using a crane simulator to evaluate its superiority and reliability.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.283-283
• /
• 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
• /
• v.56 no.5
• /
• pp.966-972
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1404-1409
• /
• 2003

The sway control problem of the pendulum motion of the container crane hanging on the trolley, which transports containers from the container ship to the truck, is considered in this paper. In the container crane control problem, the main issue is to suppress the residual swing motion of the container at the end of the acceleration, deceleration or the case of that the unexpected disturbance input exists. For this problem, in general, the trolley motion control strategy is introduced and applied to real plants. In this paper, we suggest a new type of swing motion control system for a crane system in which a small auxiliary mass is installed on the spreader. The actuator reacting against the auxiliary mass applies inertial control forces to the spreader of the container crane to reduce the swing motion in the desired manner. In this paper, we consider that the length of the rope varies is we design the anti-sway control system based on LMI(linear matrix inequality) approach. And, it will be shown that the proposed control strategy is useful and it can be easily applicable to the real world. So, in this study, we investigate usefulness of the proposed anti-sway system and evaluate system performance from simulation and experimental studies.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.131.6-131
• /
• 2001

During the operation of crane system in container yard, it is necessary to control the crane trolley position and loop length 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 stacking crane system.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.15 no.1
• /
• pp.9-16
• /
• 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
• /
• 2000.10a
• /
• pp.142-142
• /
• 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.