• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.89-96
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• 2023

The Wiener-type nonlinear model where a static nonlinear block follows a dynamic linear block is widely used to describe the dynamics of chemical processes. A long process excitation step is typically needed to identify this Wiener-type nonlinear model with two blocks. In order to cope with this disadvantage, an identification method for the Wiener-type nonlinear model that uses only a single-step response is proposed here. The proposed method estimates the response of the dynamic linear sub-block from the initial part of the step response, and then the static nonlinear sub-block is identified. Because the only single-step response is used to identify the Wiener-type nonlinear model, there is great benefit in time and cost for obtaining process response. The performance of the proposed identification method with the single-step response is verified through a representative Wiener-type nonlinear process, a pH titration process, and a liquid level system.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.97-105
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• 1996

In this paper, an adaptive model predictive controller for nodinear processes using fuzzy model is proposed. Adaptive structure is implemented by recursive fuzzy modeling. The model and control law can be obtained the same as GPC, because the consequent parts of the fuzzy model comprise linear equations of input and output variables. The proposed Adaptive fuzzy model predictive controller (AFMPC) controls nonlinear process well due to the intrinsic nonlinearity of the fuzzy model. When AFMPC's output is variation in the process control input, it maintains zero steady-state offset for a constant reference input and has superior performance. The properties and performance of the proposed control scheme were examined with nonlinear plant by simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2549-2551
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• 2005

본 논문은 제어 공정의 파라미터의 동정과 축소모델을 이용하여 선형 및 비선형 특성을 고려한 PID 제어기 설계를 제안하였다. 제어기 파라미터값은 2차의 지연시간을 갖는 축소 모델의 파라미터값에 의해 결정되며, 외란 및 제어 공정의 파라미터 값이 변할 때에는 실제 모델의 동정을 통해 구하며, 또한 실제 공정과 축소 모델의 관계식을 통해 제어 파라미터 값을 실시간으로 보정하여 제어한다. 시뮬레이션을 통하여 실시간 모델 동정 및 제어 파라미터 값이 보정됨을 확인 할 수 있다.

• ICROS
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• v.3 no.5
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• pp.58-64
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• 1997

pH 공정은 비선형이며, 입력흐름의 성분과 농도가 바뀜에 따라 비선형성이 급격히 바뀌는 적응제어가 요구되는 공정이다. 최근 이 공정에 대한 모델링 기법이 확립되고, 매우 작은 수의 변수를 갖는 parameterization이 제안되어 간단한 형태의 우수한 적응제어법이 구성되어 있다. 그러나 estimation의 windup은 선형 시스템의 적응제어법에서와 마찬가지로 큰 문제점으로 남아있다. 본 고에서는 pH 공정의 적응제어법을 간략히 살펴보았으며 좀 더 견실하고 우수한 성능을 주는 방법을 위하여 두 가지 제안을 하였다. 한 제안은 기존의 변수 estimation의 목적함수가 제어기 성능에 직접적이지 못한 것을 바로 잡으려는 것이다. 새로 제안한 것을 적응제어에 바로 이용하는데 아직 걸림돌이 몇몇 남아 있어 연구가 요구되고 있다. 또 한 제안은 estimation windup을 해결하려는 것으로 pH 공정 특성상 나타나는 것으로 바로 pH 공정 적응제어에 이용될 수 있다.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.203-209
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• 2016

In this paper, we introduce a fuzzy inference systems for nonlinear inference using fuzzy cluster. Typically, the generation of fuzzy rules for nonlinear inference causes the problem that the number of fuzzy rules increases exponentially if the input vectors increase. To handle this problem, the fuzzy rules of fuzzy model are designed by dividing the input vector space in the scatter form using fuzzy clustering algorithm which expresses fuzzy cluster. From this method, complex nonlinear process can be modeled. The premise part of the fuzzy rules is determined by means of FCM clustering algorithm with fuzzy clusters. The consequence part of the fuzzy rules have four kinds of polynomial functions and the coefficient parameters of each rule are estimated by using the standard least-squares method. And we use the data widely used in nonlinear process for the performance and the nonlinear characteristics of the nonlinear process. Experimental results show that the non-linear inference is possible.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.2
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• pp.8-12
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• 1984

Algorithms for solving a set of nonlinear simultaneous equations, which is frequently required in problems of controlling nonlinear processes, are proposed. Here the equation variables are first parameterized and a recursive identification technique is utilized. The forms and characteristics of the resultant algorithms are vary similar to the Broyden's quasi-Newton method, but their derivations and final recursion equations are different. Our methods possess almost all the merits of the Broyden's and numerical comparisons show our methods to be more efficient and reliable for some difficult problems.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.87-92
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• 2007

Recently, developments of process monitoring system in order to detect and diagnose process abnormalities has got the spotlight in process systems engineering. Normal data obtained from processes provide available information of process characteristics to be used for modeling, monitoring, and control. Since modern chemical and environmental processes have high dimensionality, strong correlation, severe dynamics and nonlinearity, it is not easy to analyze a process through model-based approach. To overcome limitations of model-based approach, lots of system engineers and academic researchers have focused on statistical approach combined with multivariable analysis such as principal component analysis (PCA), partial least squares (PLS), and so on. Several multivariate analysis methods have been modified to apply it to a chemical process with specific characteristics such as dynamics, nonlinearity, and so on.This paper discusses about missing value estimation and sensor fault identification based on process variable reconstruction using dynamic PCA and canonical variate analysis.