• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1645-1650
• /
• 2005

FOPDT(First-Order Plus Dead-Time) and SOPDT(Second-Order Plus Dead-Time) process, which are used as the most useful process in industry, are difficult about process identification because of the long dead-time problem and the model mismatch problem. Thus, the accuracy of process identification is the most important problem in FOPDT and SOPDT process control. In this paper, we proposed the real-coded genetic algorithm for identification of FOPDT and SOPDT processes. The proposed method using real-coding genetic algorithm shows better performance characteristic comparing with the existing an area-based identification method and a directed identification method that use step-test responses. The proposed strategy obtained useful result through a number of simulation examples.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.140-145
• /
• 1994

In this paper, we consider a problem to estimate process parameters using input-output data collected from the process operating in closed-loop control system. When orders and delay-time of the process are known correctly, under some conditions of identifying experiments, it is reported that accurate identification results can be obtained by applying prediction error method. To get accurate estimates, it is necessary to know orders and delay-time of the process. It is difficult to determine them in closed-loop identification, because ill-condition for identification are easily caused by selection of unsuitable order or delay time. Furthermore, the procedures to select orders and delay-time in open-loop identification aren't always available in closed-loop identification. The purpose of this paper is to determine a delay-time under suitable assumption that order of the process are known as the first step.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1008-1011
• /
• 1996

Most control systems of power plants are using classical PID controllers for their process control. In order to get the desired control performances, the correct tuning of PID controllers is very important. Sometimes, it is necessary to retune PID controllers after the change of system operating condition and system design change, etc. Commercial auto-tuning controllers such as relay feedback controller can be used for this purpose. However, using these controllers to the safety-critical systems of nuclear power plants may be cause of unsafe operation, because they are using test signals for tuning. A new system identification auto-tuning method without using test signal has been developed in this paper. This method uses process input/output signals for system identification of unknown control process. From the model information of control process which was obtained from system identification approach, the optimal PID parameters can be calculated. The method can be used in the safety-critical systems because it is not using test signals during system modeling process.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2003.11a
• /
• pp.355-359
• /
• 2003

In this paper, an identification method for a first order dead time process is proposed. This method used the genetic algorithm for parameter identification of process. The proposed method gives a better identification result than the existing methods under step testing. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.1
• /
• pp.40-46
• /
• 2000

In this paper, a transient identification based on a Hidden Markov Model (HMM) has been suggested and evaluated experimentally for the classification of transients in the dynamic process. The transient can be identified by its unique time dependent patterns related to the principal variables. The HMM, a double stochastic process, can be applied to transient identification which is a spatial and temporal classification problem under a statistical pattern recognition framework. The HMM is created for each transient from a set of training data by the maximum-likelihood estimation method. The transient identification is determined by calculating which model has the highest probability for the given test data. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM. Several experimental tests have been performed including superimposing random noise, adding systematic error, and untrained transients. The proposed real-time transient identification system has many advantages, however, there are still a lot of problems that should be solved to apply to a real dynamic process. Further efforts are being made to improve the system performance and robustness to demonstrate reliability and accuracy to the required level.

• International Journal of Quality Innovation
• /
• v.3 no.2
• /
• pp.174-182
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.61.5-61
• /
• 2002

1. Introduction 2. Development of the Proposed Identification Method 2.1 MlMO Hammerstein nonlinear process 2.2 Process activation 2.3 Identification of the linear dynamic subsystem 2.4 Identification of the nonlinear static function 3. Simulation Study 4. Conclusion. Acknowledgment. References

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.8
• /
• pp.3779-3784
• /
• 2012

Real-time process monitoring and diagnosis of industrial processes is one of important operational tasks for quality and safety reasons. The objective of fault diagnosis or identification is to find process variables responsible for causing a specific fault in the process. This helps process operators to investigate root causes more effectively. This work assesses the applicability of combining a nonlinear statistical technique of kernel Fisher discriminant analysis with a preprocessing method as a tool of on-line fault identification. To compare its performance to existing linear principal component analysis (PCA) identification scheme, a case study on a benchmark process was performed to show that the fault identification scheme produced more reliable diagnosis results than linear method.

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

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.7
• /
• pp.178-187
• /
• 1999

In order to identify a transfer function model with noise, penalty function method has been widely used. In this method, estimation process for possible model parameters from low to higher order proceeds the model identification process. In this study, based on linear estimation method, a new approach unifying the estimation and the identification of ARMAX model is proposed. For the parameter estimation of a transfer function model with noise, linear estimation method by noise separation is suggested instead of nonlinear estimation method. The feasibility of the proposed model identification and estimation method is verified through simulations, namely by applying the method to time series model. In the case of time series model with noise, the proposed method successfully identifies the transfer function model with noise without going through model parameter identification process in advance. A new algorithm effectively achieving model identification and parameter estimation in unified frame has been proposed. This approach is different from the conventional method used for identification of ARMAX model which needs separate parameter estimation and model identification processes. The consistency and the accuracy of the proposed method has been verified through simulations.