• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.161-166
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• 1990

In this paper, Proposed the robust controller for robot manipulator plus actuator with dynamic parameter uncertainties. In general, errors and uncertainties system parameters exist more or less between the actual system and mathematical model. To reduce these trems, used Lyapunov stability theorem. The performance of the controller is evaluated for the three degree of freedom robot manipulator plus actuator model with uncertainties of parameters and model errors.

• 전자공학회논문지B
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• 제28B권11호
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• pp.897-903
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• 1991

In this paper we propose a hybrid position/force controller of a robot manipulator using feedback error learning rule and neural networks. The neural network is constructed from inverse dynamics. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained well, it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using PUMA 560 manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1651-1656
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• 2005

This paper describes a natural frequency analysis conducted to find out a suitable working area for a spring-manipulator system generating a large vibrating force with mechanical resonance. Large force generation is one of the functions that we hope for a robot. For example, a weeding robot is required to generate a large force, because some weeds have roots spreading deeply and tightly. The spring-manipulator system has a spring element as an end-effector, so it can be in a state of resonance with the elasticity of the spring element and the inertial characteristics of the manipulator. A force generation method utilizing the mechanical resonance has potential to produce a large force that cannot be realized by a static method. A method for calculating a natural frequency of a spring-manipulator system with the generalized inertia tensor is proposed. Then the suitable working area for the spring-manipulator system is identified based on a natural frequency analysis. If a spring-manipulator system operates in the suitable working area, it can sustain mechanical resonance and generate a large vibrating force. Moreover, it is shown that adding a mass at the tip of the manipulator expands the suitable working area.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.552-555
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• 1997

Recently, increasing attention has been paid to the application of learning control method to robot manipulator control. Because the learning control method does not require an exact dynamic model, it is flexible and easy to implement. In this paper, we implement a learning control scheme which consists of a unique feedforward learning controller and a linear feedback controller. The learning control method does not require acceleration terms that are sensitive to noise and has the capability of rejecting unknown disturbances and adapting itself to time-varying system parameters. The feasibility of the learning control scheme is soon by implementing the control scheme to a commercial robot manipulator and the performance of which is also compared with the conventional linear PID control method.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.743-748
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• 2004

In this paper, an analysis on a new robot manipulator developed for the side buffing of the shoes is presented. The robot is composed of five D.O.F. An Analysis on the forward and inverse kinematics was performed. The hardware system including electric wirings, control system, and related system was developed. Also, The teleoperating communication system was developed to shake with other related system Computer programs to track the bonding line of shoes were developed. An user-friendly graphic program was developed using C $^{++}$ language for the users.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 추계학술대회 논문집
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• pp.545-549
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• 1993

This paper presents the method of calibrating and compensating for the kinematic errors in robot manipulators. A calibration model is developed to represent any geometric errors in the manipulator's structure. A calibration jig is used to find the values of these kinematic errors in the end-effector's position and a calibration algormined for a SSR-6 robot manipulator developed by Samsung Heavy Industry, Daeduk R & D Center. Through this experiment the maximun kinematic error is reduced from 10mm to 0.4mm

• International Journal of Safety
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• 제1권1호
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• pp.1-6
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• 2002

It is important to control the high accurate position and force to prevent unexpected accidents by a robot manipulator. Direct-drive robots are suitable to the position and force control with high accuracy, but it is difficult to design a controller because of the system's nonlinearity and link-interactions. This paper is concerned with the study of the stabilization force control of direct-drive robots. The proposed algorithm is consists of the feedback controllers and the neural networks. After the completion of learning, the outputs of feedback controllers are nearly equal to zero, and the neural networks play an important role in the control system. Therefore, the optimum adjustment of control parameters is unnecessary. In other words, the proposed algorithm does not need any knowledge of the controlled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the force control of a parallelogram link-type robot.

• Journal of Mechanical Science and Technology
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• 제18권11호
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• pp.1916-1922
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• 2004

A robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, and etc. Therefore, uncertainties are often nonlinear and time-varying. The neural network structure presents the bound function and does not need the concave property of the bound function. The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulator.

• 제어로봇시스템학회논문지
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• 제10권9호
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• pp.755-762
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• 2004

In this research, EOG(Electrooculogram) signal was analyzed to predict the subject's intention using a fuzzy classifier. The fuzzy classifier is built automatically using the EOG data and evolutionary algorithms. An assistant robot manipulator in redundant configuration has been developed, which operates according to the EOG signal classification results. For automatic fuzzy model construction without any experts' knowledge, an evolutionary algorithm with the new representation scheme, design of adequate fitness function and evolutionary operators, is proposed. The proposed evolutionary algorithm can optimize the number of fuzzy rules, the number of fuzzy membership functions, parameter values for the each membership functions, and parameter values for the consequent parts. It is shown that the fuzzy classifier built by the proposed algorithm can classify the EOG data efficiently. Intelligent motion planner that consists of several neural networks are used for control of robot manipulator based upon EOG classification results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.171.2-171
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• 2001

This paper presents a force control of a arm of walking training robot. The current gait training apparatus in hospital are ineffective for the difficulty in keeping constant unloading level and constraining patients to walk freely. The proposed walking training robot is designed to unload body weight effectively during walking. The walking training robot consists of unloading manipulator and mobile platform. The manipulator driven with a electro-mechanical linear mechanism unloads body weight in various level. The mobile platform is wheel type, which allows to patients unconstrained walking. Unloading system with electro-mechanical linear mechanism has been developed, which has advantages such as low noise level, light weight, low manufacturing cost and low power consumption. A system model for the manipulator ...