• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.679-690
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• 2011

Two widely used approaches for improving the quality of the output of a process are statistical process control (SPC) and automatic process control (APC). In recent hybrid processes that combine aspects of the process and parts industries, process variations due to both the inherent wandering and special causes occur commonly, and thus simultaneous application of APC and SPC schemes is needed to effectively keep such processes close to target. The simultaneous implementation of APC and SPC schemes is called integrated process control (IPC). In the IPC procedure, the output variables are monitored during the process where adjustments are repeatedly done by its controller. For monitoring the APC-controlled process, control charts can be generally applied to the output variable. However, as an alternative, some authors suggested that monitoring the input variable may improve the chance of detection. In this paper, we evaluate the performance of several monitoring statistics, such as the output variable, the input variable, and the difference variable, for efficiently monitoring the APC-controlled process when we assume IMA(1,1) noise model with a minimum mean squared error adjustment policy.

• Proceedings of the Korea Institute of Applied Superconductivity and Cryogenics Conference
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• 2003.10a
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• pp.146-149
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• 2003

In this Work, we have studied about an RSFQ 2$\times$2 crossbar switch. The circuit was designed, simulated, and laid out for mask fabrication The switch cell was composed of a splitter a confluence buffer, and a switch core. An RSFQ 2$\times$2 crossbar switch was composed of 4 switch cells, a switch control input to select the cross and bar, data input, and data outputs. When a pulse was input to the switch control input to select the cross or bar the route of the input data was determined, and the data was output at the proper output port. We simulated and optimized the switch-element circuit and 2$\times$2 crossbar switch, by using Xic and Julia. We also performed the mask layout of the circuit by using Xic and Lmeter.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.222-226
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• 1992

In this study, the technique of adaptive control based on certainty equibalence for input-output linerization of nonlinear system is investigated. It is shown that the upper bound of the parameter estimation error can be represented more explicitly than Teel et al's works. Another direct approach, which shows that the adaptive input-output linearing control laws using the normalized identifier yield bounded tracking is also presented.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.11
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• pp.896-904
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• 1989

PWM techniques which eliminate and reduce harmonics of output voltage in PWM Inverter driving System with fluctuating input volotage are described. First, harmonic factors are analyzed from harmonic equation of general PWM waveform and by examination of control possiblity of each factor, controllable factor is selected. Applying controllable factor to NPWM, PWM techniques using reference wave and carrier wave modulation are introduced. Actually, by the experiment applied with this strategy, the reduction of harmonics of output voltage is confirmed.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1076-1078
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• 1996

In this paper, we design the robust controller for MIMO system using multiple fuzzy logic controller. Based on the knowledge of system input/output data, we introduce the simple adaptation laws to approximate the decoupling matrix from input channel to output channel. The proposed control algorithm is applied numerical example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.194-198
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• 1998

A novel fuzzy basis function vector- based adaptive control approach for Multi-input and Multi-output(MIMO) system is presented in this paper, in which the nonlinear plants is first linearised, the fuzzy basis function vector is then introduced to adaptively learn the upper bound of the system uncertainty vector, and its output is used as the paramenters of the compensator in the sense that both the robustness and the asymptotic error convergence can be obtained for the closed loop nonlinear control system.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.689-696
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• 2000

In this paper we propose an eminent controller for wheeled mobile robots. This controller consists of an input-output linearization controller trying to stabilize the system and a neural network controller to compensate for uncertainties. The uncertainties are divided into two parts. First unstructured uncertainties include the elements related with system order such as friction disturbance. Second structure uncertainties are the incorrect system parameters A neural network structure of the proposed overall controller learns structural errors of the wheeled mobile robots with uncertainties and includes the neural network output. This controller learns quickly the model and has good tracking performance Simulation results show that the proposed controller is more efficient than analog controllers.

• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.401-407
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• 2008

The dynamic model and displacement control of piezoelectric actuators, which are commercially available materials for managing extremely small displacements in the range of sub-nanometers, are presented. Piezoceramics have electromechanical characteristics that transduce energy between the electrical and mechanical domains. However, they have hysteresis between the input voltage and output displacement, and this behavior is very demanding and complicated. In this paper, we propose a method of designing the control algorithm, and present the dynamic modeling equations that represent the hysteretic behavior between input voltage and output displacement. For this process, the piezoelectric actuator is treated as a second-order linear dynamic system and system constants are determined by the system identification method. Also, a classical PID controller is designed and used to regulate the output displacement of the actuator. To evaluate the performance of the proposed method, numerical simulation results are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.9
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• pp.822-830
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• 2000

A conventional advanced control algorithm is proposed in this paper for improved temperature regulation of a TV-glass melting furnace. The TV-Glass melting furnace is a typical MIMO(Multi-Input Multi Output) system which is subject to various thermal disturbances. Because of its complexity, a detailed mathematical model of the furnace is hard to establish. To design a temperature control control system of the furnace, major input-output variables are selected first, and simple FOPDT(First Order Plus Dead Time) models are established based on the physical meaning and experimental process data. Based on the FOPDT models, a multi-loop control system composed of cascade and single loops are designed for effective control of the MIMO system. Practical implementation on the 150 ton/day furnace using the DCS(Distributed Control System) showed that the proposed control technique performs better than manual control.