• 대한교통학회지
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• 제13권4호
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• pp.117-132
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• 1995

Peak-period congestion is a frequent occurrence on many freeways. Entrance-metering is an effective strategey in improving urban freeway traffic condition. This paper describes the prodecure to develop a computer program for proparing optimum ramp-metering stategies. Four existing ramp-metering optimaization models wer reviewed and evaluated in regared to their theories and actural performances. A optimization model was proposed in this paper. Final model takes aform of quadratic programming. The performance of the propeosed model wastest using FREFLO.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.160-163
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• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.56.5-56
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• 2002

This paper studies on the feedback linearization of the looper system for hot strip mills, where the looper system plays an important role in regulating the strip tension. Firstly, nonlinear dynamic equations of the looper system are simply introduced. Secondly, using the static feedback linearization algorithm, a linear model of the looper system is obtained, of which usefulness is validated from comparison between the linear model and the nonlinear model, and design of LQI(Linear Ouadratic Integral optimal control) and ILQ (Inverse Linear Quadratic optimal control) looper control systems. In result, it is shown that the linear looper model by the feedback linearization well describes nonlin...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.490-495
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• 1992

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• 전기학회논문지
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• 제57권6호
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• pp.1048-1057
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• 2008

This paper represents a design method for PID or low-order controllers cascaded with a linear plant in the unit feedback system where it is required to meet the given time response specifications such as overshoot and settling time. This problem is difficult to solve because the zeros of the controller appear in the numerator of the overall system and thus those zeros may make the time response design difficult. In this paper, we propose a new approach based on the partial model matching and the so called K-polynomial. The partial matching problem is formulated to an optimization problem in which a quadratic function of coefficient errors between a target model and the resulting closed loop system is minimized. For the sake of satisfying the closed loop stability, a set of quadratic constraints associated with the cost function is introduced. As a result, the controller designed meets both time response requirements and the closed loop stability, if any. It is shown through several examples that the present method can be easily applied to these problems.

• Journal of the Korean Data and Information Science Society
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• 제25권1호
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• pp.177-185
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• 2014

로지스틱회귀모형에서 설명변수만으로는 충분히 설명이 되지 못하고 설명변수의 변환된 형태인 이차항 또는 교호작용항이 필요한 경우가 있다. 설명변수가 두 개이고 조건부 분포가 이변량 정규분포를 따르는 경우 로지스틱회귀모형에서는 기본적으로 이차항과 교호작용항이 모형에 포함되어야 한다. 하지만 조건부 분포의 분산과 상관계수에 따라 이차항과 교호작용항이 필요하지 않게 되는 경우도 있다. 분산이나 상관계수에 대한 정보는 산점도를 보고 대체적인 판단이 가능하지만 교호작용항의 필요성을 판단하기가 쉽지 않다. 본 논문에서는 3차원 잔차산점도를 이용한 교호작용의 탐색방법을 제시하고 이 방법을 실제 자료에 적용시켜본다.

• Journal of Power Electronics
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• 제8권1호
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• pp.10-22
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• 2008

The wind-driven doubly fed induction generator (DFIG) is currently under pressure to be more grid-compatible. The main concern is the fault ride-through (FRT) requirement to keep the generator connected to the grid during faults. In response to this, the paper introduces a novel model and new control scheme for the DFIG. The model provides a means of direct stator power control and considers the stator transients. On the basis of the derived model, a robust linear quadratic (LQ) controller is synthesized. The control law has proportional and integral actions and takes account of one sample delay in the input owing to the microprocessor's execution time. Further, the influence of the grid voltage imperfection is mitigated using frequency shaped cost functional method. Compensation of the rotor current pulsations is proposed to improve the FRT capability as well as the generator performance under grid voltage unbalance. As a consequence, the control system can achieve i) fast direct power control without instability risk, ii) alleviation of the problems associated with the DFIG operation under unbalanced grid voltage, and iii) high probability of successful grid FRT. The effectiveness of the proposed solution is confirmed through simulation studies on 2MW DFIG.

• International Journal of Precision Engineering and Manufacturing
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• 제1권1호
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• pp.79-83
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• 2000

Although it is well known that fuzzy learning controller is powerful for nonlinear systems, it is very difficult to apply a learning method if they are unstable. An unstable system diverges for impulse input. This divergence makes it difficult to learn the rules unless we can find the initial rules to make the system table prior to learning. Therefore, we introduced LQR(Linear Quadratic Regulator) technique to stabilize the system. It is a state feedback control to move unstable poles of a linear system to stable ones. But, if the system is nonlinear or complicated to get a liner model, we cannot expect good results with only LQR. In this paper, we propose that the LQR law is derived from a roughly approximated linear model, and next the fuzzy controller is tuned by the adaptive on-line learning with the real nonlinear plant. This hybrid controller of LQR and fuzzy learning was superior to the LQR of a linearized model in unstable nonlinear systems.

• Journal of Welding and Joining
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• 제10권4호
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• pp.190-198
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• 1992

Among the variety of welding processes available, the flux cored arc welding is one of the most frequently used process, because of its wide range of application and high productivity. The weld joint tracking is indispensable to improve the flexibility of the arc welding robot application for the flux cored arc welding (FCAW) process. In this study, an arc sensor which utilizes the electrical signal obtained from the welding arc itself was developed for weld joint tracking in FCAW. Because a model of the welding arc in flux cored arc welding was required to develop the arc sensor, a mathematical model was proposed by analysing the welding arc behaviour, and also an experimental model by using the factorial experiment and least square method. For overcoming the fluctuation in the welding current signal during tracking the weld joint, it was fitted to a curve which is inversely proportional to a trace of tip-to-workpiece distance by using the quadratic curve-fitting method.

• 소성∙가공
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• 제25권3호
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• pp.182-188
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• 2016

Flexibly-reconfigurable roll forming (FRRF) is a novel sheet metal forming technology conducive to producing multi-curvature surfaces by controlling the strain distribution along longitudinal direction. In FRRF, a sheet metal is shaped into the desired curvature by using reconfigurable rollers and gaps between the rollers. As FRRF technology and equipment are under development, a simulation model corresponding to the physical FRRF would aid in investigating how the shape of a sheet varies with input parameters. To facilitate the investigation, the current study exploits regression analysis to construct a predictive model for the longitudinal curvature of the sheet. Variables considered as input parameters are sheet compression ratio, radius of curvature in the transverse direction, and initial blank width. Samples were generated by a three-level, three-factor full factorial design, and both convex and saddle curvatures are represented by a quadratic regression model with two-factor interactions. The fitted quadratic equations were verified numerically with R-squared values and root mean square errors.