• Journal of Power Electronics
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• v.11 no.1
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• pp.45-50
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• 2011

This paper proposes a method for comparing and evaluating anti-windup proportional-integral (PI) control strategies. The so-called PI plane is used and its coordinate is composed of the error and the integral state. In addition, an anti-windup PI controller with integral state prediction is proposed. The anti-windup scheme can be easily analyzed and evaluated on the PI plane in detail. Representative anti-windup methods are experimentally applied to the speed control of a vector-controlled induction motor driven by a pulse width modulated (PWM) voltage-source inverter (VSI). The experimental results compare the anti-windup PI controllers. It is empathized that the initial value of the integral state at the beginning of the linear range dominates the control performance in terms of overshoot and settling time.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.903-906
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• 1997

When large capacity generation stations that consist of several large units tend to pull out of step from main power system, stabilizing control scheme as emergency control for preventing loss of synchronism of the whole stations with the remaining system is devided into two steps that the first step is to perform on-line prediction for out-of-step and the next step is on-line calculation of the amount of generation shedding for the rest of generators to be in step when out of step is expected. This paper presents on-line prediction scheme for out-of-step based on P-$\delta$ curve estimation using real-time measurement and on-line calculation of generation shedding. The proposed stabilizing scheme was applied to case study of real power system and the results obtained by the method compare well with the results by simulation.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.6
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• pp.89-95
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• 2000

In order to achieve effective prediction of residual stresses, the series experiment were carried out and the residual stresses were measured using the backgpropagation algorithm from the neural network and the sectional method. Using the experimental results, the optimal control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances on residual stresses during GMA welding processes. The results obtained from the comparison between the measured and calculated results, showed that the neural network based on backpropagation algorithm can be sued in order to control weld quality. This system can not only help to understand the interaction between the process parameters and residual stress, but also, improve the quantity control for welded structures. The development of the system is goal in this study.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.135-141
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• 2008

A new approach to $H_{\infty}$ multi-step prediction is developed by applying the innovation analysis theory. Although the predictor is derived by resorting to state augmentation, nevertheless, it is completely different from the previous works with state augmentation. The augmented state here is considered just as a theoretical mathematic tool for deriving the estimator. A distributed algorithm for the Riccati equation of the augmented system is presented. By using the reorganized innovation analysis, calculation of the estimator does not require any augmentation. A numerical example demonstrates the effect in reducing computing burden.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1021-1024
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• 1993

System identification is a major component for a control system. In biosystems, which is nonlinear and dynamic, precise identification would be very helpful for implementing a control system. It is difficult to precisely identify such non-linear systems. The measurable data on products from 2,3-butanediol fermentation could not be included in a process model based on kinetic approach. Meanwhile, a predictive capability is required in developing a control system. A neural network (NN) dynamic identifier with a by/(1＋ t ) transfer function was therefore designed being able to predict this fermentation. This modified inverse NN identifier differs from traditional models in which it is not only able to see but also able to predict the system. A moving window, with a dimension of 11 and a fixed data size of seven, was properly designed. One-step ahead identification/prediction by an 11-3-1 BPNN is demonstrated. Even under process fault, this neural network is still able to perform several-step ahead prediction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9A
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• pp.1299-1305
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• 2000

In B-ISDN, to realize ATM, the optimum control method of multi-media traffic must be proposed. Because there is not the traffic model of multi-media to make clear, the realization of optimum ATM congestion control is very difficult. In this paper, the traffic model is assumed to be slowly time-variable non-linear function and for real-time prediction of it, new model which is composed with parallel triple neural networks is proposed. And the simulation to predict assumed ATM traffic is executed. From the result, it's capability is shown that the proposed neural network model can be used in ATM congestion control.

• Journal of Astronomy and Space Sciences
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• v.26 no.2
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• pp.267-278
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• 2009

Automation of the key flight dynamics operations for the geostationary orbit satellite mission is analyzed and designed. The automation includes satellite orbit determination, orbit prediction, event prediction, and fuel accounting. An object-oriented analysis and design methodology is used for design of the automation system. Automation scenarios are investigated first and then the scenarios are allocated to use cases. Sequences of the use cases are diagramed. Then software components and graphical user interfaces are designed for automation. The automation will be applied to the Communication, Ocean, and Meteorology Satellite (COMS) flight dynamics system for daily routine operations.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.9
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• pp.191-199
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• 1999

In the continuous cutting process such as turning operation, chip control is thought very important to achieve the unmanned manufacturing system. The prediction of chip breakage under the given conditions is a substantial element for chip control. In this paper, a systematic approach to know the chip breaking region is represented under the concept of equivalent parameters. to Verify the suggested model, cutting experiments are executed with a commercial type and two other type chip breakers which have modified chip breaker parameters such as land width, groove width and nose radius. predicted chip breaking regions using the 3D cutting model agrees with those obtained from the experiments.

• Earthquakes and Structures
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• v.16 no.6
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• pp.679-690
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• 2019

This paper presents machine learning methods using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) to analyze optimal damper distribution for structural vibration control. Regarding different building structures, a genetic algorithm based optimization method is used to determine optimal damper distributions that are further used as training samples. The structural features, the objective function, the number of dampers, etc. are used as input features, and the distribution of dampers is taken as an output result. In the case of a few number of damper distributions, multi-class prediction can be performed using SVM and MLP respectively. Moreover, MLP can be used for regression prediction in the case where the distribution scheme is uncountable. After suitable post-processing, good results can be obtained. Numerical results show that the proposed method can obtain the optimized damper distributions for different structures under different objective functions, which achieves better control effect than the traditional uniform distribution and greatly improves the optimization efficiency.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.768-783
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• 2020

Typically, a Dynamic Positioning System (DPS) uses a PID feed-back system, and it often adopts a wind feed-forward system because of its easier implementation than a feed-forward system based on current or wave. But, because a ship's drifting motion is caused by wind, current, and wave drift loads, all three environmental loads should be considered. In this study, a motion predictive control for the PID feedback system of the DPS is proposed, which considers the three environmental loads by utilizing predicted drifted ship positions in the future since it contains information about the three environmental loads from the moment to the future. The prediction accuracy for the future drifted ship position is ensured by adopting deep learning algorithms and a replay buffer. Finally, it is shown that the proposed motion predictive system results in better station-keeping performance than the wind feed-forward system.