• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1327-1332
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• 2003

In this paper, an ATC(Automated Transfer Crane) control system is required rapid transportation to get highest productivity with low cost. Therefore, the container paths should be built in terms of the least time and least sway when container is transferred from the initial coordinate to the finial coordinate. So we applied the best-first search method for forming the container path, and calculated the anti-collision path for avoiding collision in its movement to the finial coordinate. And we constructed the neural network two degree of freedom PID (TDOFPID) controller to control the precise navigation. For simulation, we constructed the container profiles so that we analyzed the state of formed path and the performance of TDOFPID controller to the formatted path. Then we compared the performance of ES-tuned PID controller with our proposed controller in terms of trolley position, anti-sway, path change, disturbance, and the load of containers. The computer simulation results show that the proposed controller has better the other on the various conditions.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1304-1307
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• 1996

In this paper, we present a robust Two Degree of Freedom (TDF) $H_{\infty}$ controllers for a missile system. The feedback controller is designed to meet robust stability and disturbance rejection specifications while the prefilter is used to improve the robust model matching properties of the closed loop system. As the perturbed model, we use the normalized coprim factor perturbations. These controllers are designed using $H_{\infty}$ optimization procedures, and applied to a missile model via simulation.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.2
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• pp.74-80
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• 2003

In this paper, a robust two-degree-of-freedom(TDF) the speed control system using $H_{\infty}$ optimization method and real genetic algorithm is proposed for the robust stability and the robust performance in dc servo motor system. This control system composed of feedback and feedforward controller. The feedback(FB) controller with $H_{\infty}$ optimization method is designed for real genetic algorithm that is model matching problem using mixed sensitivity function. The feedforward(FF) controller with $H_{\infty}$optimization method is minimized the error between transfer function of the optimal model and the overall transfer function. The proposed robust two-degree-of-freedom speed control system is simulated to the dc servo motor. By the simulation, feedback controller can obtain the robust stability property and feedforward controller can obtain the robust performance property under modelling error. The performance of the dc servo motor is analyzed by the experiment setting. The validity of the proposed method is verified through being compared with pid(proportional integrated differential)control system design method for the dc servo motor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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• pp.426-431
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• 1997

In this paper, a sensorless realization method is proposed for the magnetic levitation system. Also we design the robust servo controller which based on the two degree-of-freedom-control theory and H$\sub$.inf./ control theory for the system. From time responses, we confirm that the proposed sensorless method can be applied the magnetic levitation system. Also the designed controller has the good disturbance rejection and the reference tracking performance.

• 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.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.670-672
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• 1995

This paper deals with the robust two-degree-of-freedom multivariable control system using $H_{2}/H{\infty}$optimization method which can achieve the robust stability and the robust performance, simultaneously. The feedback controller can obtain the robust stability property. The feedforward controller can obtain the robust performance property under modelling error. The robust two-degree-of-freedom multivariable control system is applied to the nonlinear multivariable boiler-turbine system. The validity of the proposed method is verified though being compared with LQG/LTR design method.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.980-986
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• 1999

In this paper, the optimal controller which decouples a coupled multivariable system and minimizes a quadratic performance index is proposed. Design of the controller is based on the two-degree-of-freedom standard model. The class of all stabilizing and decoupling controllers is parametrized first and the $H^{2}$optimal controller is obtained by using this parametrized form. An illustrative example for a $2{\times}2$ plant is given.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.193-196
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• 2000

ln this paper, we consider to apply of 2-DOF (Degree of Freedom) PID controller at D.C servo motor system. Many control system use I-PD, PIB control system. but the position control system have difficulty in controling variable load and changing parameter We propose neural network 2-DOF PID control system having feature for removal disturbrances and tracking function in the target value point.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.56-61
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• 2011

Many control techniques have been proposed in order to improve the control performance of the control system. The degree of freedom on control in the control system is decided by the number of the closed-loop transfer function which can adjust independently. The controller design scheme with two degrees of freedom(TDOF) is extensively used for securing the good control performance to trace a desired value and reject disturbance. In this paper, PID controller is used by controller with TDOF and the design method for control system with TDOF is proposed by the normal method. Using the coefficients of the transfer function of the plant, the transfer function of the control system is normalized by the proposed design method and the parameters of the controller are determined. The control system with the TDOF is constructed by using this method. Through the simulation results, the usefulness of the proposed algorithm is proved.