• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2898-2900
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• 2000

A sliding mode control algorithm combined with an adaptive scheme, which is used to estimate the unknown parameter bounds. is developed for the trajectory control of robot manipulators. Simulated results show the validity to accurate tracking capability and robust performance.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1671-1676
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• 1991

Direct digital design of computed torque controllers for a robot manipulator is discussed in this paper. A simple discrete-time model of the robot manipulator obtained by Euler's method is used for the design. Taking account of computation delay in the digital processor, we propose predictor-based designs of the PD and PID type controllers. The PID type controller is designed based on a modified version of the discrete-time integral controller proposed by Mita. For both controllers, the same formulas can be used to determine the feedback gains. A simulation example is presented to compare the robustness of the proposed controllers against physical parameter variations.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.396-399
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• 1994

In a large class of industrial robot manipulators, its end effector for supporting the moving object have designed with mechanical suspension method(gripper). In this paper, We describe a high performance magnetically levitated end effector of robot, where is no mechanical contact and friction.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.990-995
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• 1991

The integration of intelligent robots into manufacturing systems should positively impact the product quality and productivity. A new theory of object location and recognition using the supporting plane is presented. The unknown supporting points are determined by image coordinates, known camera parameters, and joint coordinates of the robot manipulators. This is developed by using the geometrical interpretation of perspective projection and the geometrical constraints of industrial environments. This can be applied to solve typical robot vision problems such as determination of position, orientation, and recognition of objects.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.65-68
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• 1996

Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. The TMS32OC31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the, network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time, control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.347-355
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• 2002

Obstacle avoidance of underactuated robot manipulators using switching computed torque method (SCTM) is presented. One fundamental feature of this novel method is to use partly stable controllers (PSCs) in order to fulfill the ultimate control objective. Here, we use genetic algorithms (GAs) to acquire the optimum switching sequence of the control actions for a given time frame with the available set of elemental controllers, depending on which links/variables are controlled. The effectiveness of the concept is illustrated by taking a three-degrees-of-freedom (DOF) manipulator and showing enhanced performance of the proposed control methodology.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.30 no.4
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• pp.329-340
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• 1994

This paper presents a dynamic compensation methodology for robust trajectory tracking control of uncertain robot manipulators. To improve tracking performance of the system, a full model-based feedforward compensation with continuous VS-type robust control is developed in this paper(i.e,. robust decentralized adaptive control scheme). Since possible bounds of uncertainties are unknown, the adaptive bounds of the robust control is used to directly estimate the uncertainty bounds(instead of estimating manipulator parameters as in centralized adaptive control0. The global stability and robustness issues of the proposed control algorithm have been investigated extensively and rigorously via a Lyapunov method. The presented control algorithm guarantees that all system responses are uniformly ultimately bounded. Thus, it is shown that the control system is evaluated to be highly robust with respect to significant uncertainties.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.269-274
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• 1990

Usually, robot manipulators in production lines are operated with reperting work trajectories. This paper presents the repeated adaptive learning algorithm for robot manipulates for the case of a trajectory. This algorithm uses the nonlinear dynamic model including the repeated friction compensating term, The advantage of the scheme is that It allows friction compensation which may be otherwise difficult for differently constructed models. A secondary advantage of the sheme is that it can also adapt to torque calculation in order to reduce the computational load of the control computer. To show the efficiency of the proposed controller, a computer simulation is performed for the planar robot manipulator with a 2 degree of freedom.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.61-65
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• 1997

In control of complex system, like robot manipulators, BP neural network have several drawbacks. To overcome this problems, the modified BP neural networks have proposed To find neural network of proper structure for robot manipulator, in this paper, actual experiments using ADSP-21020 for SCARA robot were implemented and have shown the possibility of real-time control and industrial application, without neural chip.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.323-329
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• 1994

In the systems such as walking robots or high speed operating manipulators, the effect of nonlinear terms is important and can not be neglected. Therefore the application of linear control law to such systems is inadequate. Moreover, because of the mathematical modeling errors the systems may become unstable. In this study, we designed a nonlinear controller with sliding mode scheme, which is robust to the modeling errors and applied this control algorithm to the 5 DOF biped robot system. Throught the computer simulations, we examined walking characteris and walking stability of the 5 DOF biped robot system.