• 전자공학회논문지B
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• 제32B권12호
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• pp.1545-1551
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• 1995

As the modern industrial processes become more complex, it is getting more difficult to model and control the processes. Naturally, an advanced type of DCS(Distributed Control System) with higher level functions is being sought. Advanced DCS is a DCS with advanced functions such as fault diagnosis, GPC(Generalized Predictive Control), NN(Neural Network), and Fuzzy Control. In this thesis, we have studied a fuzzy control algorithm for realizing an advanced DCS. Its algorithm is implemented in a form of function code which is a process control language, being used by the industrial engineers. To verify the realized function code of the fuzzy control, the function code is applied to a continuous casting process of the Pohang Iron & Steel Works in Kwangyang. The rules of the fuzzy control were collected via interviews of the field operators and their operation documents. Finally under a real-time operating system environment, usability of the function code of the fuzzy control is shown via simulation for the continuous casting process.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.995-998
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• 1995

As the modern industrial processes become more complex, it is getting more difficult to model and control the processes. Naturally, an advanced type of DCS(Distributed Control System) with higher level functions is being sought Advanced DCS is a DCS with advanced functions such as fault diagnosis, GPC(Generalized Predictive Control), NN(Neural Network), and Fuzzy Control. In this thesis, we have studied a fuzzy control algorithm for realizing an advanced DCS. Its algorithm is implemented in a form of function code which is a process control language, being used by the industrial engineers. To verify the realized function code of the fuzzy control, the function code is applied to a continuous casting process of the Pohang Iron & Steel Works in Kwangyang. The rules of the fuzzy control were collected via interviews of the field operators and their operation documents. Finally, usability of the function code of the fuzzy control is shown via simulation for the continuous casting process model.

• 설비공학논문집
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• 제12권11호
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• pp.1038-1047
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• 2000

The objective of this study is to develop an optimized Artificial Neural Network(ANN) model to predict the descending time of room air temperature. For this, program for predicting room air temperature and ANN program using generalized delta rule were collected through simulation for predicting room air temperature. ANN was trained and the ANN model having the optimized values-learning rate, moment, bias, number of hidden layer, and number of neuron of hidden layer was presented.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.327-330
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• 1996

It is well known that the controller output limits have a signifiant effect on the closed loop system performance. Considering the input constraints in GPCF, an effective selection method of the control weighting(.gamma.) is proposed to reduce the amplitude and the rate of control signals so that control signals lie within the limits. It is based on the relation between control weighting(.gamma.) and optimal solution of the unconstrained GPCF. The GPCFIC algorithm chooses an .gamma. at each sampling time so that all unconstrained GPCF output over the control horizon satisfy the rate and the amplitude constraints. In order to evaluate the performance of the GPCFIC, the computer simulations have been done for level control of PWR steam generator in low power operation and shown satisfactory results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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• pp.376-379
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• 1989

In this paper a direct adaptive control scheme which minimizes N-step cost function is proposed. It is derived via GPC control algorithm under the assumption that we know N-step unit step response of the given system. The proposed adaptive scheme is shown to have the same stability properties with the RHTC control, and require very small amount of calculation compared with other adaptive algorithms which minimize N-step cost function. And it is shown through simulations that the proposed adaptive algorithm is robust to the variation of the unit step response which is assumed to be known.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1992년도 춘계학술대회 논문집
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• pp.385-389
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• 1992

The position control of the electro-hydraulic servo indexing system in a flexible forging machine was investigated. The role of an Electro-hydraulic servoindexing system is to rotate a workpiect fast and accurately to a desired position. Since the inertia of a workpiece changes during each forging step, an adapt control scheme could be adopted to the position control of the workpiece. A generalized predictive control method which depends on predicting the plant output over several steps ahead based on the assumption about future control actions is proposed. The perormance of the proposed GPC algorithm is investigated experimentally and compared with that of PID control. Experimental results show that the proposed GPC algorithm is satisfactory for the position control of the workpiece of an electrohydraulic indexing system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1196-1198
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• 1996

It is well known that the controller output limits have a significant effect on the closed loop system performance. GPC has many tuning-knobs which can he used to minimize actuator activity. Especially, increasing the control weighting $\lambda$ cuts down the controller output variance. Using this property, we propose the GPC with Input constraints(GPCIC) which is based on the relation between control weighting $\lambda$ and optimal solution of the unconstrained GPC. The GPCIC algorithm is the calculation of the optimal $\lambda$ such that the output of the unconstrained GPC is satisfied with the rate Ind the level constraint.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.261-264
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• 1993

This paper proposes an adaptive generalized predictive control with feedforward algorithm for steam generator level control in nuclear power plant. The proposed algorithm is shown that the parameters of N-step ahead predictors can be obtained using the parameters of one-step ahead predictor which is derived from plant model with feedforward. Using this property the proposed scheme is an adaptive algorithm which consists of GPC method and the recursive least squares algorithm for identifying the parameters of one-step ahead predictor. Also, computer simulations are performed to evaluate the performance of proposed algorithm using a mathematical model of PWR steam generator Simulation results show good performances for load variation. And the proposed algorithm shows better responses than PI controller does.

• Journal of Power Electronics
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• 제12권5호
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• pp.800-812
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• 2012

This paper analyses the mathematical model and control strategies of a Hybrid Active Power Filter with Injection Circuit (IHAPF). The control strategy based on the load harmonic current detection is selected. A novel control method for a IHAPF, which is based on the analyzed control mathematical model, is proposed. It consists of two closed-control loops. The upper closed-control loop consists of a single fuzzy logic controller and the IHAPF model, while the lower closed-control loop is composed of an Adaptive Network based Fuzzy Inference System (ANFIS) controller, a Neural Generalized Predictive (NGP) regulator and the IHAPF model. The purpose of the lower closed-control loop is to improve the performance of the upper closed-control loop. When compared to other control methods, the simulation and experimental results show that the proposed control method has the advantages of a shorter response time, good online control and very effective harmonics reduction.

• 한국정밀공학회지
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• 제26권5호
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• pp.120-127
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• 2009

An electric furnace, inside which desired temperatures are kept constant by generating heat, is known to be a difficult system to control and model exactly because system parameters and response delay time vary as the temperature and position are changed. In this study the heating system of ceramic drying furnaces with time-varying parameters is mathematically modeled as a second order system and control parameters are estimated by using a RIV (Recursive Instrumental-Variable) method. A modified bang-bang control with magnitude tuning is proposed in the time optimal temperature control of ceramic drying electric furnaces and its performance is experimentally verified. It is proven that temperature tracking of adaptive time optimal control using a second order model is more stable than the GPCEW (Generalized Predictive Control with Exponential Weight) and rapidly settles down by pre-estimation of the system parameters.