• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.341-346
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• 2004

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.1
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• pp.85-92
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• 1994

With the wide application of fiber-reinforced composite meterial in aircraft space structures and robot arms, the design and manufacture of composite joints have become a very important research area because they are often the weakest areas in composite structure. In this paper, the torque transmission capacities of the adhesive tubular single lap joint and double lap joint were studied. The stress and torque transmission capacity of the adhesive joints were analyzed by the finite element method and compared to the experimental results. The torque capacity of the double lap joint was increased 2.7 times over that of the single lap joint. Also, the fatigue limit of the double lap joint was increased 16 times over that of the single lap joint.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.754-758
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• 2001

A new kind of piezoelectric actuation structure named double amplified multilayer actuator is proposed. Double amplified multilayer actuator combines both dimentional and flextensional amplification concepts. As a result the displacement of the actuator can be more than one hundred times larger than the displacement of multilayer actuator and can be used in in-pipe locomation robot such as an endoscope actuator. This paper studied the dependence of displacement on actuator parameters theoretically and experimentally.

• Journal of the Optical Society of Korea
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• v.16 no.3
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• pp.221-227
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• 2012

We propose optical proximity corrections (OPCs) for digital micromirror device (DMD)-based maskless lithography. A pattern writing scheme is analyzed and a theoretical model for obtaining the dose distribution profile and resulting structure is derived. By using simulation based on this model we were able to reduce the edge placement error (EPE) between the design width and the critical dimension (CD) of a fabricated photoresist, which enables improvement of the CD. Moreover, by experiments carried out with the parameter derived from the writing scheme, we minimized the corner-rounding effect by controlling light transmission to the corners of a feature by modulating a DMD.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.38-44
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• 2001

This paper investigates the direct adaptive control of nonlinear systems using RBFN(radial basis function networks). The structure of the controller consists of a fixed PD controller and a RBFN controller in parallel. An adaptation law for the parameters of RBFN is developed based on the Lyapunov stability theory to guarantee the stability of the overall control system. The filtered tracking error between the system output and the desired output is shown to be UUB(uniformly ultimately bounded). To evaluate the performance of the controller, the proposed method is applied to the trajectory contro of the two-link manipulator.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.469-472
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• 2016

Jansen Mechanism has been a constant popularity by researchers studying legged robots because of many benefits. This paper proposed the design process of optimized legged robots using Jansen Mechanism and m.Sketch(Jansen Mechanism simulation software). First, driving part of legged robots is designed in compliance with the design regulations of a competitive exhibition. Second, setting the length of link and position of joint is conducted in keeping with the constraints. Third, Ground Length (GL) and Ground Angle Coefficient(GAC) values are extracted by m.Sketch simulation. Finally, by repeating the previous procedures, comparing the GL and GAC values, find the optimum input values. This.

• Journal of Sensor Science and Technology
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• v.13 no.1
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• pp.27-34
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• 2004

Camera calibration is an important and fundamental procedure for the application of a vision sensor to 3D problems. Recently many camera calibration methods have been proposed particularly in the area of robot vision. However, the reliability of data used in calibration has been seldomly considered in spite of its importance. In addition, a camera model can not guarantee good results consistently in various conditions. This paper proposes methods to overcome such uncertainty problems of data and camera models as we often encounter them in practical camera calibration steps. By the use of the RANSAC (Random Sample Consensus) algorithm, few data having excessive magnitudes of errors are excluded. Artificial neural networks combined in a two-step structure are trained to compensate for the result by a calibration method of a particular model in a given condition. The proposed methods are useful because they can be employed additionally to most existing camera calibration techniques if needed. We applied them to a linear camera calibration method and could get improved results.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.255-263
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• 2012

This paper presents the smart interacting multiple model (SIMM) using the concept of predicted point and maximum noise level. Maximum noise level means the largest value of the mere noises. We utilize the positional difference between measured point and predicted point as acceleration. Comparing this acceleration with the maximum noise level, we extract the acceleration to recognize the characteristics of the target. To estimate the acceleration, we propose an optional algorithm utilizing the proposed method and the Kalman filter (KF) selectively. Also, for increasing the effect of estimation, the weight given at each sub-filter of the interacting multiple model (IMM) structure is varying according to the rate of noise scale. All the procedures of the proposed algorithm can be implemented by an on-line system. Finally, an example is provided to show the effectiveness of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1444-1446
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• 2005

Recently most machineries have been small size and mobile type. And human body insertion type endoscope and micro robot technology has been developed. Then the motors used in this field are developed in micro size such as about 2mm in diameter. The structure of this motor is similar to a general brushless DC(BLDC) motor but because of small size there is no position sensor such as hall sensor. In this paper, a design and fabrication result of an ultra-small brushless DC motor is presented. This motor is designed to 3-phase coreless winding and operated with sensor-less type driver. Test results confirmed the feasibility of the proposed motor drive system design.