• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.836-839
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• 1997

In recent years, much progress has been made in the automation of welding coped with a variety of highly flexible sensors. Among these sensors, only the eddy current sensor can detect the center location of the butt joint whose gap distance is zero. Thus, in this study the eddy current sensor is used to develop a robust and useful joint tracking system. The developed system is tested to qualify the performance of the system and seam tracking algorithm is proposed and two simulation are executed to show the performance of the proposed tracking algorithm.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.93-100
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• 2003

Accuracy in maneuvering target tracking using multiple models relies upon the suit-ability of each target motion model to be used. To construct multiple models, the interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require predefined sub-models and predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers. To solve these problems, this paper proposes the GA-based IMM method as an intelligent tracking method for a maneuvering target. In the proposed method, the acceleration input is regarded as an additive process noise, a sub-model is represented as a fuzzy system to compute the time-varying variance of the overall process noise, and, to optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. The simulation results show that the proposed method has a better tracking performance than the AIMM algorithm.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.3
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• pp.244-250
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• 2002

This paper proposed a modified observer based on Busawon's high gain observer using an appropriate time depended function, which can be chosen to make each estimated state converge faster to its real value. The stability of the modified observer is proved by using Lyapunov function. The modified nonlinear observer is applied to estimate the states in stirred tank bioreactor: out-put substrate concentration, output biomass concentration and the specific growth rate of the process. The convergences of the modified observer and Busawon's observer are compared trough simulation results. As the results, the modified observer converges faster to its real value than the well-known Busawon's observer.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.182-188
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• 2000

High-frequency electric resistance welding (HERW) technique is one of the most productive manufacturing method currently available for pipe and tube production because of its high welding speed. In this process, a heat input is controlled by skilled operators observing color and shape of bead but such a manual control can not provide reliability and stability required for manufacturing pipes of high grade quality because of a variety of bead shapes and noisy environment. In this paper, in an effort to provide reliable quality inspection, we propose a neural network-based method for classification of bead shape. The proposed method utilizes the structure of Kohonen network and is designed to learn the skill of the expert operators and to provide a good solution to classify bead shapes according to their welding conditions. This proposed method is implemented on the real pipe manufacturing process, and a series of experiments are performed to show its effectiveness.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1101-1106
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• 2005

The purpose of this study was to develop a process management system to manage ingot fabrication and the quality of the ingot. The ingot is the first manufactured material of wafers. Operating data (trace parameters) were collected on-line but quality data (measurement parameters) were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Thus, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were employed for data generation, and then modeling was accomplished, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to the control parameters. The dynamic polynomial neural network (DPNN) was used for data modeling that used the ingot fabrication data.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.35-42
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• 2007

This paper presents an INS(Inertial Navigation System) grade, surface micro-machined differential resonant accelerometer(DRXL) manufactured by an epitaxially grown thick poly silicon process. The proposed DRXL system generates a differential digital output upon an applied acceleration, in which frequency transition is measured due to gap dependent electrical stiffness change. To facilitate the resonance dynamics of the electromechanical system, the micromachined DRXL device is packaged by using the wafer level vacuum sealing process. To test the DRXL performance, a nonlinear self-oscillation loop is designed based on the extended describing function technique. The oscillation loop is implemented using discrete electronic elements including precision charge amplifier and hard feedback nonlinearity. The performance test of the DRXL system shows that the sensitivity of the accelerometer is 24 Hz/g and its long term bias stability is about 2 mg($1{\sigma}$) with dynamic range of ${\sigma}70g$.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.570-576
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• 2007

This paper presents a hexagon-based Q-leaning algorithm to find a hidden targer object with multiple robots. An experimental environment was designed with five small mobile robots, obstacles, and a target object. Robots went in search of a target object while navigating in a hallway where obstacles were strategically placed. This experiment employed two control algorithms: an area-based action making (ABAM) process to determine the next action of the robots and hexagon-based Q-learning to enhance the area-based action making process.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.577-583
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• 2007

Simultaneous Localization and Mapping (SLAM) is the process of building a map of an unknown environment and simultaneously localizing a robot relative to this map. SLAM is very important for the indoor navigation of a mobile robot and much research has been conducted on this subject. Although feature-based SLAM using an Extended Kalman Filter (EKF) is widely used, it has shortcomings in that the computational complexity grows in proportion to the square of the number of features. This prohibits EKF-SLAM from operating in real time and makes it unfeasible in large environments where many features exist. This paper presents an algorithm which reduces the computational complexity of EKF-SLAM by using topological information (TI) extracted through a thinning process. The global map can be divided into local areas using the nodes of a thinning-based topological map. SLAM is then performed in local instead of global areas. Experimental results for various environments show that the performance and efficiency of the proposed EKF-SLAM/TI scheme are excellent.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.594-600
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• 2007

A sufficient condition for the robust D-stability of dynamic interval systems is proposed in this paper. This D-stability condition is based on a parameter-dependent Lyapunov function obtained from the feasibility of a set of matrix inequalities defined at a series of partial-vertex-based interval matrices other than the total vertex matrices as previous results. This condition is also extended to the robust D-stabilization problem of dynamic interval systems, which supplies an effective synthesis procedure for any LMI D-region. The proposed conditions can be simplified to a set of LMIs, which can be solved by efficient interior point methods in polynomial time.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.