• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.899-902
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• 1993

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 combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.733-743
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• 1989

This paper deals with the control of system with controlled jump Markov disturbances. A such formulation was used by Boukas to model the planning production and maintenance of a FMS with failure machines. The optimal control problem of systems with controlled jump Markov process is addressed. This problem describes the planning production and preventive maintenance of production systems. The optimality conditions in both cases finite and infinite horizon, are derived. A numerical example is presented to validate the proposed results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.129-138
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• 2016

The train-centric control systems development has some distinct points in that a big size of government budget is in general expended in there and the development duration seems to be long. In addition, the changes are ever made continuously in the capability and operational requirements for Trains. Thus, the impact of the potential changes in the required operational capability on the development activities can induce some type of project risks [7]. As such, proper management of project risk has been one of crucial subjects in the train systems development. All these notes combined together make it the significance of the safety management process be raised further up in the train-centric control systems development. As such, the underlying safety management process should be capable of appropriately handling the potential risks that can be created due to the unexpected changes and the long-term development period. The process should also be complemented for the safety consideration of train-centric control systems, for instance, stop. To study these aspect is the objective of the paper. To do so, a step-by-step approach to analyzing the safety management process is first presented. Then, to enhance the process some necessary and useful activities are added in terms of risk and safety management. Then, to pursue some enhancement on the process, a set of necessary and useful activities are added in terms of risk and safety management. The resultant process is further analyzed and tailored using a design structure matrix method. The resultant process is applied in a train-centric control development as a case study.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.139-147
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• 2009

Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM ($\bar{x}$, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM ($\bar{x}$, s) control chart are considered.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.398-402
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• 1987

In the process industries control & organization consist of interconnections on different levels. The process controls that run these industries are a collection of well-defined functions in the form of standard modules interconnected by a communication network. Real-Time(throughput, response time), operator communication, flexibility, back-up and recovery needs have distribute organization of both system hardware and software. Multi-level systems are aften advocated for controlling complex systems, such as, electric, water, oil, gas plants. In practice, these systems encompass computers and person with their various communication requirements and limitations. Hence, Careful mutual adaptation of computer communications and organizational structure is necessary. This paper concentrates on these interactions between process control and organization on the basis of industrial case studies.

• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1074-1085
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• 2004

There are many intermediate web guides in cold rolling mills process such as CRM (cold rolling mill), CGL (continuous galvanizing line), EGL (electrical galvanizing line) and so on. The main functions of the web guides are to adjust the center line of the web (strip) to the center line of the steel process. So they are called CPC (center position control). Rapid process speed cause large deviation between the center position of the strip and the process line. Too much deviation is not desirable. So the difference between the center position of the strip and the process line should be compensated. In general, the center position control of the web is obtained by the hydraulic driver and electrical controller. In this paper, we propose modelling and several controller designs for web-guide systems. We model the web and guide by using geometrical relations of the guide ignored the mass and stiffness of the web. To control the systems, we propose PID controllers with their gains tuned by the Ziegler-Nichols method, the H$\_$$\infty$/ controller model-matching method, and the coefficient diagram method (CDM). CDM is modified for high order systems. The results are verified by computer simulations.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.112-117
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• 2013

The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.1
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• pp.33-40
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• 2010

Because sampling interval for data collection tends to be short compared with the overall processing time, in chemical process, instrumental process related tanks or furnace collected data have a significant autocorrelation. Insufficient control technique and frequent control actions cause unstable condition of the process. Traditional control charts which were developed based on iid (independently and identically distributed) among data cannot be applied on the existence of autocorrelation. Also unstable process is difficult to identity or diagnose. Because large-scale process has a lot of measurable variables and multi-step-structures among data, it is difficult to find relation between measurable variables and nonconformity. In this paper, we suggested an appicable model to diagnose the process and to find relation between measurable variables (CTQ) and nonconformity in the process having autocorrelation, unstable condition frequently, a lot of measurable variables, and multi-step-structure. And we applied this model to real process, to verify that the process engineers could easily and effectively diagnose the process and control the nonconformity.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.1
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• pp.47-57
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• 2010

There have been many problems to apply SPC(Statistical Process Control) which is a traditional process control technology to the process of multi-product, small-sized production because a machine in the process manufactures small numbers, but various kinds of products. Therefore, we need the new process control system that can flexibly control the process by setting up the SPEC rules and the KNOWHOW rules. The SPEC rule contains the combination of diverse conditions to specify the characteristics of various products. The KNOWHOW rule is based on engineers' know-how. The study suggests the Rule-base Process Control that can be optimized to the multi-product, small-sized production. It was validated in the process of semiconductor production.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.8
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• pp.24-34
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• 1992

A process control language is developed using function block configuration, to simplify software development for large scale process control systems, and to implement advanced control algorithms with ease. A function block parser and controller is implemented to be suitable for multi-loop control systems having hierachical structure. On-line change of controller parameter is possible, and inclusion of user defined function block is also possible. By adding plant model block, control performance can be checked in advance. Function blocks of the Smith Predicotor, auto-tuners are implemented to demonstrate usefulness of function block configuration.