• International Journal of Quality Innovation
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• 제3권2호
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• pp.174-182
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• 2002

System identification and controller formulation are essential in dynamic process control. In system identification, data for system identification are obtained, and then they are analyzed so that the system model of the process is built, identified, and diagnosed. In controller formulation, the control equation is derived based on the result of the system identification. There has been much theoretical research on system identification and controller formulation. These theories are very useful when they are appropriately applied. To our regret, however, these theories are not always effectively applied in practice because the engineers and the operators who manage the process often do not have the necessary understanding of required time series analysis methods. On the other hand, because of widespread use of statistical packages, system identification such as estimating ARMA models can be done with little understanding of time series analysis methods. Therefore, it might be said that the most theoretically difficult part in practice is the controller formulation. In this paper, lists of control equations are proposed as a useful tool for practitioners to use. The tool supports bridging the gap between theory and practice in dynamic process control. Also, for some models, the generalized control equations are obtained.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.643-648
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• 2003

The problem considered in this paper is that the tip position of a flexible cantilever beam is controlled to follow a command signal, using a pair of piezoelectric actuators at the clamped end. The beam is lightly damped and so the natural transient response is rather long, and also since the sensor and actuator are not collocated, the plant response is non-minimum phase. Two control strategies were investigated. The first involved conventional PID control in which the feedback gains were adjusted to give the fastest closed-loop response to a step input. The second control strategy was based on an internal model control (IMC) architecture. The control filter in the IMC controller was a digital FIR device designed to minimize the expectation of the mean square tracking error. The IMC controller designed fur the beam was found to have very much reduced settling times to a step input compared with those of the PID controller.

• Journal of the Korean Data and Information Science Society
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• 제22권4호
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• pp.679-690
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• 2011

통계적 공정관리 (statistical process control; SPC)와 자동공정관리 (automatic process control; APC)는 공정의 품질을 향상시키기 위하여 가장 널리 사용하는 방법이다. 이 두 종류의 관리절차는 서로 독립적으로 적용되고 연구되어져 왔지만, 현대의 생산 공정은 공정 자체가 복잡하고 혼합된 양상을 나타내기 때문에 두 관리절차를 병행하여 사용함으로써 관리효과를 증대시킬 수 있게 된다. 이와 같이 수정과 탐지를 동시에 사용하여 공정을 좀 더 효율적으로 관리하고자 하는 절차를 통합 공정관리 (integrated process control; IPC)라고 한다. IPC의 기본절차는 잡음이 내재하는 공정에 대하여 수정조치를 취하고, 이러한 수정활동 중 공정에 이상원인이 발생했는지 관리도를 통하여 이를 탐지하는 것이다. APC로 조정된 공정을 관리할 경우 일반적으로 출력변수를 관리통계량으로 사용하고 있으나, 입력변수를 관리통계량으로 사용하는 연구 결과들도 있다. 이 논문에서는 누적이동평균(integrated moving average; IMA) (1,1) 잡음모형과 최소평균제곱오차 (minimum mean square error; MMSE) 수정을 가정할 경우, 출력변수, 입력변수, 그리고 출력변수와 입력변수의 정보를 모두 이용하는, 즉 출력과 입력변수의 차이변수를 사용하는 절차의 효율을 비교하고 있다.