• Korean Chemical Engineering Research
• /
• v.55 no.6
• /
• pp.767-770
• /
• 2017

Quadruple tank liquid level systems are popular in testing multivariable control systems for multivariable processes with positive or negative zeros. The liquid level system is nonlinear and it will help to illustrate the robustness of control systems. However, due to nonlinearity, it can be cumbersome to obtain process parameters for testing linear control systems. Perturbation sizes are limited for valid linearized process models, requiring level sensors with high precision. A simple method where the outlet orifice is replaced to a long tube is proposed here. The effluent flow rate becomes proportional to the liquid level due to the friction loss of long tube and the liquid level system shows near linear dynamics. It is applied to the quadruple tank system for easier experiments.

• Journal of the Korean Institute of Gas
• /
• v.3 no.1
• /
• pp.27-32
• /
• 1999

Chemical processes are highly nonlinear, multivariable systems and have complex structures. However, the controllability evaluation procedures are complicated, and the required information is very often unknown at the early design stage. Therefore, it is necessary to develop a procedure to evaluate and enhance controllability while designing processes and plants. To evaluate controllability in the design stage, it is most efficient to analyze process structure. Relative order can be used as a measure of 'physical closeness' between input and output variable. Structural controllability analysis using relative order is shown to be effective in a case study of heat exchanger network synthesis.

• 제어로봇시스템학회:학술대회논문집
• /
• 1999.10a
• /
• pp.45-48
• /
• 1999

Model predictive control algorithm requires a relevant model of the system to be controlled. Unfortunately, the first principle model describing a polymerization reaction system has a large number of parameters to be estimated. Thus there is a need for the identification and control of a polymerization reactor system by using available input-output data. In this work, the polynomial auto-regressive moving average (ARMA) models are employed as the input-output model and combined into the nonlinear model predictive control algorithm based on the successive linearization method. Simulations are conducted to identify the continuous styrene polymerization reactor system. The input variables are the jacket inlet temperature and the feed flow rate whereas the output variables are the monomer conversion and the weight-average molecular weight. The polynomial ARMA models obtained by the system identification are used to control the monomer conversion and the weight-average molecular weight in a continuous styrene polymerization reactor It is demonstrated that the nonlinear model predictive controller based on the polynomial ARMA model tracks the step changes in the setpoint satisfactorily. In conclusion, the polynomial ARMA model is proven effective in controlling the continuous styrene polymerization reactor.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2612-2616
• /
• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
• /
• 2004.04a
• /
• pp.97-106
• /
• 2004

The paper making process itself is a typical nonlinear process with complicated dynamics. In the application of advanced control-methods especially for the grade change operations the nonlinear process is linearized to give suitable linear models to be used in the control strategies. However, the use of the linear model is limited within short range containing steady-state operating conditions for grade change operation. In this paper a bilinear model for the nonlinear grade change processes is presented. We can see that the dynamic behavior for grade change operations can be effective analyzed by using multivariable bilinear model.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.55-59
• /
• 1991

This paper deals with the control stratege for the constant voltage, constant frequency(CVCF) generation of doubly-fed induction generator. As an induction machine is a nonlinear and multivariable machine, so, the control system is needed a very sophiticated control processes to meet a CVCF condition. In this paper, control system is constructed and tested using the suggested exitation equation. The test results show that the suggested equation and control system are very useful strategy for the CVCF control of induction generator.

• Proceedings of the KIEE Conference
• /
• 1999.11c
• /
• pp.497-499
• /
• 1999

This paper considers enhancing the robustness of a MIMO(Multi-Input Multi-Output) predictive control system. The characteristic polynomial matrix of the closed-loop is shown to consist of two factors $P_c$ and T, where $P_c$ is determined by the tuning knobs of the predictive controller and T is an observer or prefilter polynomial matrix. The robust stability condition is derived in terms of $P_c$ and T. A guideline on the selection of T is then presented for open-loop stable processes.

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

• Journal of Welding and Joining
• /
• v.11 no.4
• /
• pp.44-56
• /
• 1993

The arc welding processes are substantially nonlinear, in addition to being highly coupled multivariable systems, Frequently, not all the variables affecting the welding quality are known, nor may they be easily quantified. From this point of view, decoupling between the welding parameters from the welding quality is very difficult, which makes it also difficult to control the welding parameters for obtaining the desired welding quality. In this study, a neural network based on the backpropagation algorithm was implemented and adopted for the selection of gas metal arc welding parameters of the fillet joint, that is, welding current, arc voltage and welding speed. The performance of the neural network for modeling the relationship between the welding quality and welding parameters was presented and evaluated by using the actual welding data. To obtain the optimal neural network structure, various types of the neural network structures were tested with the experimental data. It was revealed that the neural network can be effectively adopted to select the appropriate gas metal arc welding parameter of fillet joints for a given weld quality.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.13 no.4
• /
• pp.44-55
• /
• 2004

In order to meet the requirement for higher thickness accuracy in tandem cold rolling processes, it is strongly necessary to have good performance in control units. To meet this requirement, this paper suggested an output regulating control system with a roll-eccentricity estimator for each rolling stand of tandem cold mills. Considering entry thickness variation and roll eccentricity simultaneously as the major disturbances, a synthesis of multivariable control systems was presented based on H$\infty$ control theory, which could reflect the knowledge of input direction and spectrum of disturbance signals on design. Then, to effectively reject roll eccentricity, a weight function having some poles on the imaginary axis was introduced. This lead to a non-standard H$\infty$ control problem, and the design procedures for solving this problem were analytically presented. The effectiveness of the proposed control method was evaluated through computer simulations and compared to that of the conventional linear quardratic control and feedforward control methods for roll eccentricity.