• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.370-373
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• 1988

Althouah there have been many attempts to control weld quality in resistance spot welding processes, design method for an on-line feedback controller based upon process dynamics has not been suggested. This is due to the fact that the resistance spot welding is a highly complicated process, whice involves the interaction of electrical, thermal, mechanical and metallurgical phenomena. In this paper, an optimal control method based on FDM model with shunt effect is presented, which can regulate the nugget size, at the same time minimizing the control heat input. Optimal PI gain of the controller were determined by numerical optimization. Simulation results show that, as a result of the proposed optimal control, the weld nugget can be made to approach a desired nugget size with less control heat input than that required for the conventional spot welding process in the face of the shunt effect.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2617-2622
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• 2003

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach is suffered from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis (1996). The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks and etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the global approximator, the neural network which requires training and k-nearest neighbor method which requires querying instead of training are investigated. The global approximator requires optimal control strategy. If the optimal control strategy is not available, suboptimal control strategy can be used even though the laborious Bellman iterations are necessary. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.

• 전기의세계
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• v.26 no.2
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• pp.84-88
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• 1977

In this paper we apply the maximum principle to design of time optimal nonlinear sampled-data control systems. We introduce the general design procedures and the mathematical formalas for time optimal processes and trajectories. Then we show the application of the technique to determine the optimal control signal, control sequence, switching time and sampling period to the given 4th order process.

• Proceedings of the Korean Society for Quality Management Conference
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• 2007.04a
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• pp.302-309
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• 2007

To conduct statistical process control needs the assumption that the process data are independent. However, most of chemical processes, like a semi-conduct processes do not satisfy the assumption because of autocorrelation. It causes abnormal out of control signal in the process control and misleading process capability. In this study, we introduce that Shore's method to solve the problem and to find the optimal subgroup size to estimate variance for AR(l) model. Especially, we focus on finding an actual subgroup size for small samples using simulation. It may be very useful for statistical process control to analyze process capability and to make a Shewhart chart properly.

• Journal of Korean Society for Quality Management
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• v.32 no.3
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• pp.109-125
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• 2004

This paper deals with an optimal process control problem in production of hull structural steel plate with high defective rate. The main quality characteristic(dependent variable) is the internal quality(defect) of plates and is dependent on process parameters(independent variables). The dependent variable(quality characteristics) has three categorical ordinal data and there are 35 independent variables(29 continuous variables and 6 categorical variables). In this paper, we determine the main factors and to develop the mathematical model between internal quality predicted probabilities and the main factors. Secondly, we find out the optimal process condition of main factors through analysis of variance(ANOVA) using simulation. We consider three models to obtain the main factors and the optimal process condition: linear, quadratic, error models.

• Korean Journal of Chemical Engineering
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• v.36 no.3
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• pp.356-367
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• 2019

This work presents an advanced and systematic approach to analytically design the optimal parameters of a two parameter second-order internal model control (IMC) filter that satisfies operational constraints on the output process, the manipulated variable as well as rate of change of the manipulated variable, for a first-order plus dead time (FOPDT) process. The IMC parameters are designed to minimize a control objective function composed of the weighted sum of the error between the process variable and the set point, and the rate of change of the manipulated variable, and to satisfy the desired constraints. The feasible region of the constrained IMC control parameters was graphically analyzed, as the process parameters and the constraints varied. The resulting constrained IMC control parameters were also used to find the corresponding industrial proportional-integral controller parameters of a Smith predictor structure.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1012-1015
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• 1996

This study focuses on the optimal operation and control strategy of the fuel cell process. The control objective of the Phosphoric Acid Fuel Cell (PAFC) is established and dynamic modeling equations of the entire fuel cell process are formulated as discrete-time type. On-line optimal control of the MIMO system employs the direct decomposition-coordination method. The objective function is modified as the tracking form to enhance the response capability to the load change. The weight factor matrices Q,R, which are design parameters, are readjusted. This control system is compared with LQI method and the results show that the suggested method is better than the traditional method in pressure difference control.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.563-573
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• 2007

Optimal design of an automotive catalytic converter for minimization of cold-start emissions is numerically performed using a micro genetic algorithm for two optimization problems: optimal geometry design of the monolith for various operating conditions and optimal axial catalyst distribution. The optimal design process considered in this study consists of three modules: analysis, optimization, and control. The analysis module is used to evaluate the objective functions with a one-dimensional single channel model and the Romberg integration method. It obtains new design variables from the control module, produces the CO cumulative emissions and the integral value of a catalyst distribution function over the monolith volume, and provides objective function values to the control module. The optimal design variables for minimizing the objective functions are determined by the optimization module using a micro genetic algorithm. The control module manages the optimal design process that mainly takes place in both the analysis and optimization modules.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.560-565
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• 1986

For the control of chemical process, optimal value of the process should be known at first. And process simulation is the previous step of optimal value calculation. However it is not a simple work to analyze chemical process system. Especially for the large scale chemical process system, many difficulties such as non-linearity and complexity caused by recycle streams should be overcome. In this paper, three strategies of large scale chemical process analysis were explained and discussed with case studies.