• International Journal of Advanced Culture Technology
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• v.6 no.3
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• pp.187-192
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• 2018

Permeate alteration technique is compounded the steeple sway-tuner status of the gleam-differential realization level (GDRL) on the permeate realization morph. The realization level condition by the permeate realization morph system is associated with the sway-tuner system. As to search a dot of the dot situation, we are gained of the permeate value with character-dot by the output signal. The concept of realization level is composed the reference of gleam-differential level for alteration signal by the permeate tuner morph. Moreover displaying a steeple alteration of the GDRL of the average in terms of the sway-tuner morph, and permeate dot tuner that was the a permeate value of the far alteration of the $Per-rm-FA-{\alpha}_{AVG}$ with $14.63{\pm}1.23units$, that was the a permeate value of the convenient alteration of the $Per-rm-CO-{\alpha}_{AVG}$ with $8.28{\pm}0.97units$, that was the a permeate value of the flank alteration of the $Per-rm-FL-{\alpha}_{AVG}$ with $3.28{\pm}0.58units$, that was the a permeate value of the vicinage alteration of the $Per-rm-VI-{\alpha}_{AVG}$ with $0.51{\pm}0.10units$. The sway tuner will be to evaluate at the steeple ability of the sway-tuner morph with character-dot by the permeate realization level on the GDRL that is displayed the gleam-differential morph by the realization level system. Sway realization system will be possible to control of a morph by the special signal and to use a permeate data of sway tuner level.

• 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.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.1
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• pp.35-41
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• 2006

In this paper, we develop anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2-DOF PID controller. The experimental results jar an ATC simulator show that the proposed control scheme guarantees performances, trolley position, sway angle, and settling time in NNP PID controller than other controller. As a result, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.505-519
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• 2005

In this paper, we develop an anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2 DOF PID controller. The simulation and experimental results show that the proposed control scheme guarantees performances, trolley position, sway angle and settling time in NNP PID controller than other controller. As the results in this paper, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard. Accordingly, the proposed algorithm in this study can be readily used for industrial applications.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.61-72
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• 2005

In this paper, we develop anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2 DOF PID controller. The experimental results for an ATC simulator show that the proposed control scheme guarantees performances, trolley position, sway angle, and settling time in NNP PID controller than other controller. As a result, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard. Accordingly, the proposed algorithm in this study can be readily used for industrial applications

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.346-349
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• 2003

For a traveling crane, various control methods such as neural network predictive control and TDOFPID(Two Degree of Freedom Proportional Integral Derivative) are studied. So in this paper, we proposed improved navigation method to reduce transfer time and sway with anti-collision path for avoiding collision in its movement to the finial coordinate. And we constructed the NNPPID(Neural Network Predictive PID) controller to control the precise move and speedy navigation. The proposed predictive control system is composed of the neural network predictor, TDOFPID controller, and neural network self-tuner. We analyzed ASC(Automated Stacking Crane) system and showed some computer simulations to prove excellence of the proposed controller than other conventional controllers.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.159-167
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• 2000

During the operation of crane system in the 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 and weight change. In this paper, we 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.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1907-1908
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• 2006

Recently, an automatic crane control system is required with high speed and rapid transportation. Therefore, when container is transferred from the initial coordinate to the finial coordinate, the container paths should be built in terms of the least time and without sway. Therefore, we calculated the anti-collision path for avoiding collision in its movement to the finial coordinate in this paper. And we constructed the neural network predictive two degree of freedom PID controller to control the precise navigation. The proposed predictive control system is composed of the neural network predictor, two degree of freedom PID controller, neural network self-tuner which yields parameters of two degree of freedom PID. We analyzed crane system through simulation, and proved excellency of control performance over the conventional controllers.

• 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.

• Journal of Navigation and Port Research
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• v.26 no.5
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• pp.537-542
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• 2002

Recently, an automatic crane control system is required with high speed and rapid transportation. Therefore, when container is transferred from th intial coordinate to the finial coordinate, the container paths should be built in terms of the least time and no swing. So in this paper, we calculated the anti-collision path for avoiding collision in its movement to the finial coordinate. And we constructed the neural network predictive PID (NNPPID) controller to control the precise navigation. The proposed predictive control system is composed of the neural network predictor, PID controller, neural network self-tuner which yields parameters of PID. Analyzed crane system through simulation, and proved excellency of control performance than other conventional controllers.