• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.359-359
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• 2000

Generally, level control in the steam generator of a nuclear power plant is difficulty process control, because the low power operating can lead nonminimum phase characteristics(swell and shrink phenomenon) and flow measurement are unreliable and nonlinear characteristics. This paper presents a framework for solving this problem based on the constrained linear model predictive control and introduces the design of method for the level of the controller in the entire operating power of the steam generator, and compares with conventional PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.90-95
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• 2003

In this paper, an Adaptive Nonlinear Constrained Model Predictive Control (ANCMPC) is presented for a pH control in a fed-batch bio-reactor. The pH model is represented with Hammerstein Model. The static nonlinear part of Hammerstein model is described with the static pH model, and the dynamic linear part of the Hammerstein model is described with the CARIMA model. The parameters of the CARIMA model is estimated on-line with the input and output measurements of the system using a recursive least squares type of identi�cation algorithm. The e�ectiveness of the proposed controller is shown through simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.302-305
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• 1996

This paper presents a visual servoing combined by Neural Network with optimal structure and predictive control for robotic manipulators to tracking or grasping of the moving object. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we want to predict the updated position of the object. The Kalman filter is used to estimate the motion parameters, namely the state vector of the moving object in successive image frames, and using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. The validity and effectiveness of the proposed control scheme and predictive control of moving object will be verified by computer simulation.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.13 no.6
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• pp.284-289
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• 2003

The electric furnace, inside which the desired temperature is kept by the generated heat, is known to be a difficult system to control and model exactly because system parameters and response delayed time are varied as the temperature and positions are changed. In this study, the GPCEW (generalized predictive control with exponential weight), which always guarantees the stability of the closed loop system and can be effectively applied to the internally unstable system, was introduced to the ceramic drying electric furnace and was verified by showing its temperature tracking performance experimentally.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.166.5-166
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• 2001

In the teleoperation system, force, position and velocity signals are communicated between master and slave arm. The addition of force feedback for the teleoperation system benefits the operator by providing more information to perform given tasks especially for tasks requiring contact with environment. When the master and slave arms are located in different places, time delay is unavoidable. Also it is well known that the system can become unstable when a time delay exists in the communication channel. The proposed control strategy is to use predictive control method(MBPC). The predictive controller is used to control teleoperation´s position and force control. Also it is used to overcome time delay.

• 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 Automotive Technology
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• v.6 no.3
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• pp.243-249
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• 2005

To improve the ride comfort and handling characteristics of a vehicle, an active suspension which is controlled by external actuators can be used. An active suspension can control the vertical acceleration of a vehicle and the tire deflection to achieve the desired suspension goal. For this purpose, Model Predictive Control (MPC) scheme is applied with the assumption that the preview information of the oncoming road disturbance is available. The predictive control approach uses the output prediction to forecast the output over a time horizon and determines the future control over the horizon by minimizing the performance index. The developed method is applied to a half car model of four degrees-of-freedom and numerical simulations show that the MPC controller improves noticeably the ride qualities and handling performance of a vehicle.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.383-388
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• 2011

In this paper, we consider Hammerstein-Wiener nonlinear model for solid oxide fuel cell (SOFC). A nonlinear model predictive control (MPC) is proposed to trace the constant stack terminal power by Hydrogen flow as control input. After the stability of the closed-loop system with static output feedback controller is analysed by Lyapunov method, a nonlinear model predictive control based on the Hammerstein-Wiener model is developed to control the stack terminal power of the SOFC system. Simulation results verify the effectiveness of the proposed control method based on the Hammerstein-Wiener model for SOFC system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.66-75
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• 1994

In this paper, we present a new GPC algorithm for plants with known disturbances. Also, based on the above algorithm unidirectional and bidirectional predictive controllers have been proposed concerning plants with input couplings between subprocesses. These predictive control algorithms can be applied to many industrial processes as a multiloop controllers. Simulation results show that the proposed algorithm has improved performance and computing time compared to conventional GPC algorithm.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.243-246
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• 1998

A predictive controller is designed based upon stochastic methods for compensation of network time delay which caused by the spatial separation between controllers and actuators. Current commands are generated by using time varying probability functions which can be defined according to the values of previous control inputs and actual outputs. To demonstrate the effect of this control methodology, simulation experiments are performed. The results show that even an unstabilized system by a long time delay can be stabilized with this predictive controller.