• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.83-85
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• 2007

A new approach on identifying a first-order plus time-delay (FOPTD) model using finite--duration pulse inputs has been presented recently [1]. The identification methods are very simple because it is enough to observe only two extremes and the time when they occur in the transient response to pulse input. However, when there is mismatch between actual system and FOPTD model. how sensitive the methods are has not been studied. In this paper, we investigate robustness issue of those identification algorithms in the presence of the model structure mismatch and uncertainties. Through an example we will demonstrate it.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1412-1415
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• 1996

In this paper, a new identification method of the cascade control system is proposed which can overcome the weak points of Krishnaswamy and Rangaiah(1987)'s method. This new method consists of two steps. One is on-line process identification using the numerical integration to approximate the two process dynamics with a high order linear transfer function. The other is a model reduction technique to derive out low order transfer function(FOPTD or SOPTD) from the obtained high order linear transfer function to tune the controller using usual tuning rules. While the proposed method preserves the advantages of the Krishnaswamy and Rangaiah(1987)'s method, it has such a simplicity that it requires only measured input and output data and simple least-squares technique. Simulation results show that the proposed method can be a promising alternative in the identification of cascade control systems.

• Korean Chemical Engineering Research
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• v.46 no.5
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• pp.949-957
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• 2008

Estimating the first order plus time delay model on the basis of the step responses has been widely used in industry for the tuning of PID controllers. Even though various model identification methods from simple graphical approaches to complicated approaches based on least squares method have been proposed, simple approaches to incorporate noisy step responses are rarely available. In this research, we will compare and analyze recent approaches using the integrals of the step responses and develop an improved identification method to incorporate real situations more effectively.