• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.168-172
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• 2001

The accuracy problem of robot manipulators has long been one of the principal concerns in robot design and control. A practical and economical way of enhancing the manipulator accuracy, without affecting its hardware, is kinematic calibration. In this paper an effective and practical method is presented for kinematic calibration of Stewart platforms. In our method differential errors in kinematical parameters are linearly related to differential errors in the platform pose, expressed through the forward kinematics. The algorithm is tested using simulated measurement in which measurement noise is included.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1471-1474
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• 2002

In this paper, we propose a robust indirect adaptive fuzzy state feedback regulator based on Takagi-Sugeno fuzzy model. The proposed adaptive fuzzy regulator is less sensitive to singularity than the conventional one based on the feedback linearization method. Furthermore, the proposed control method is applicable to not only plants with a perfect model but also plants with an imperfect model, which causes uncertainties. We verify the global stability of the proposed method by using Lyapunov method. In order to support the achievement, the application of the proposed adaptive fuzzy regulator to the control of a nonlinear system under the external disturbance is presented and the performance was verified by some simulation result.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.40-50
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• 1996

An unified compliant control algorithm to regulate the force by dual arms is proposed, where tow arms are treated as one arm in a kinematic viewpoint. The force error calculated form the information of two force/torque sensors attached to the end of each arm is transferred to minimum actuator coordinates, and then is distributed to total system actuator coordinates. The position adjustment at the total actuator coordinates is computed based on the effective computed based on the effective compliance matrix with respect to total actuator coordinates, which is obtained by coordinate transformation between the task coordinates and the total actuator coordinates. An experiment is carried out for dual arms with asymmetric kinematic structure to control an interaction force between manipulators and the environment. The performances of the proposed control algorithm are experimentally compared to those of dual arms employing master/slave scheme. The proposed compliant control algorithm not only ouperforms other algorithms, but also can be treated as an unified approach n the sense that it can be applied to arbitrary dual arm systems with general kinematic structures.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.17-25
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• 2014

This paper presents the formulation of an impedance controller for regulating the contact force with the environment. To achieve an accurate force tracking control, uncertainties in both robot dynamics and the environment require to be addressed. As part of the framework of the proposed force tracking formulation, a neural network is introduced at the desired trajectory to compensate for all uncertainties in an on-line manner. Compensation at the input trajectory leads to a remarkable structural advantage in that no modifications of the internal force controllers are required. Minimizing the objective function of the training signal for a neural network satisfies the desired force tracking performance. A neural network actually compensates for uncertainties at the input trajectory level in an on-line fashion. Simulation results confirm the position and force tracking abilities of a robot manipulator.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2874-2876
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• 2000

본 연구에서는 [1]에서 제안된 알고리즘을 마찰력이 존재하는 로봇 매니퓰레이터에 적용하여 위치제어를 수행하였다. 제안된 제어 알고리즘은 로봇의 위치 정보만을 이용하는 반복적 제어구조를 가지고 있다. 또한 제어기 설계 시, 로봇의 파라미터를 이용하지 않기 때문에 파라미터 불확실성이 있는 시스템에 강인한 특징을 보인다. 제안된 제어기의 성능을 검증하기 위하여.마찰력이 존재하는 2축 SCARA형 로봇에 적용하여 시뮬레이션 수행하였다

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.595-597
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• 2005

This paper shows the development of remote control system for manipulators which consists of PHANToM Device as a master, Samsung FARA robot as a slave and TCP/IP based LAN fortheir Communication. This work includes the motion mapping between the master and the slave, Generation of virtual viscosity force preventing operator's unwilled action and 3D remote control simulators for the stable operation of the remote control system, etc. The remote control implementation has been performed and the results shows that the developed system can allow the operator to effectively control the manipulator.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2729-2731
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• 2000

In this paper, we propose an adaptive controller using RBFN(radial basis function network) for robot manipulators. The structure of the proposed controller consists of a RBFN and a fixed gain PD controller. On the basis of the Lyapunov stability theorem, we guarantee the UUB (uniformly ultimately boundedness) for the total system. And the learning law of RBFN is established by the Lyapunov method. Finally, we apply the proposed controller to tracking control for the 2 link SCARA type robot manipulator.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2910-2912
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• 2000

Since the heavy-duty power manipulator has high ratio gear reducers at its joints, its dynamic characteristics are much slower than that of the master manipulator and it is likely to encounter the saturation in the input magnitude when it is used as the slave manipulator in telemanipulator systems. This paper proposes a force reflecting position control scheme for 2 axes heavy-duty power manipulator which compensates control input saturation. A series of experiments is shown to give an excellent tracking performance regardless of saturation.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.651-653
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• 1998

This thesis propose the prefilter type control strategies using modified chaotic neural networks #or the trajectory control of robotic manipulator. Since the structure of chaotic neural networks and neurons, chaotic neural networks can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis PUMA robot is designed by CNN. The CNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on- line learning and the performance is excellent. The CNN controller have much better controllability and shorter calculation time compared to the RNN controller. Another advantage of the proposed controller could be attached to conventional robot controller without hardware changes.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.558-560
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• 1998

The operation of robot manipulators has a restriction that the operator must reside at the factory, where the manipulator is used. To overcome this restriction, we propose a remote control system using the internet, the system which runs on the Window 95 environment is composed of the remote client which transfers commands to the server which control and manage the manipulator in the factory. In the control of Hong-ik Direct Drive Arm, it is necessary to consider the complex nonlinear parameters causing the mutual interaction between joints, so we use two TMS320C31 DSP chips in the controller for the real time dynamic control algorithms. For the test of system integrity and the verification of the mathematical modeling, we apply CTM, PD and VSS control algorithms and the simulation results are satisfactory.