• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.1
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• pp.99-110
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• 1995

An industrial robot needs a simple and robust control algorithm obtaining high precision control performance in spite of disturbance and parameter's change. In this paper, for solving this problem, a new sliding mode control algorithm is proposed and applied to the trajectory control of a SCARA type robot. The proposed algorithm has diminished the chattering occurring in sliding mode by setting a dead band along the switching line on the phase plane. It shows that we can easily obtain a simple switching control input satisfying sliding mode in spite of regarding nonlinear terms of a manipulator and servo system as disturbance. A guideline for selection of dead-band width is determined by optimal value of cost function presenting magnitudes of chattering and error. By this algorithm, we can expect the high performance of the trajectory tracking of an industrial robot which needs a robust and simple algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.2
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• pp.119-128
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• 2015

In this paper, we propose a new motion control technology to design robust control system of industrial robot. The system modeling of robotic manipulation tasks with constraints is presented, and the control architecture for unconstrained and constrained motion system with parametric uncertainties is synthesized. The optimal reference of robot manipulator is generated by the reference controller as a discrete state system and the control behavior of constrained system which has poor modeling information and time-invariant constraint function is improved motion control system is successfully evaluated by experiment to the desired tasks.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.344-346
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• 1992

In this paper a robust hybrid control algorithm for n-link nonredundant robot manipulators is proposed. This scheme includes an estimation law for the upper bound on the uncertainty such that robust control input is updated as a function of the estimated upper bound. The uniform ultimate boundedness of the tracking error is generated by the Lyapunov based theory.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.517-522
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• 2000

When a real robot manipulator is mathematically modeled. uncertainties are not avoidable. The uncertainties are often nonlinear and time-varying. The uncertain factors collie from imperfect knowledge ok system parameters. payload change. friction. external disturbance. and etc. In this paper. we propose a class of robust hybrid controls of manipulators without knowing the exact stiffness and provide the stability analysis. Simulation results are provided to show the effectiveness of the algorithms.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.265-270
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• 2001

In this paper, a robust controller is proposed to control a robot manipulator which is governed by highly nonlinear dynamic equations. The controller is computationally efficient since it does not require the dynamic model or parameter values of a robot manipulator. It, however, requires uncertainty bounds which are derived by using properties of revolute joint robot dynamics. The stability of the robot with the controller is proved by using Lyapunov's direct method. The results of computer simulations also show that the robot system is stable, and has excellent trajectory tracking performance.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.193-200
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• 2004

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.393-400
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• 2002

Teleoperation enables an operator to manipulate remote objects. One of the main goals in teleoperation researches is to provide the operator with the fueling of the telepresence, being present at the remote site. For these purposes, a master robot must be designed as a bilateral control system that can transmit position/force information to a slave robot and feedback the interaction force. A newly proposed impedance algorithm is applied for the control of a haptic interface that was developed as a master robot. With the movements of the haptic interface for position/force commands, impedance parameters are always varying. When the impedance parameters between an operator and the haptic interface and the dynamic model are known precisely, many model based control theories and methods can be used to control the device accurately. However, due to the parameters'variations and the uncertainty of the dynamic model, it is difficult to control haptic interfaces precisely. This paper presents a robust adaptive impedance control algorithm for haptic interfaces.

• Journal of Drive and Control
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• v.12 no.3
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• pp.1-10
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• 2015

In this paper, the authors propose a method to easily recognize and reproduce the robot motion made by an operator. This method is targets for applications similar to painting and welding, and it is based on a process of that identifies a family of plants, by control design and by conducting an experimental evaluation. In this study, the models and controllers for all joints of 3DOF robot system are obtained individually. And a robust control system for motion control of the individual joints is designed based on $H_{\infty}$ control framework. An experimental comparison is made between the proposed control method and existing PID control method. And the results indicate that the proposed designing method is more efficient and useful than conventional method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.150-155
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• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using digital signal processors for robot manipulators. TMS3200C50 is used in implementing real-time adaptive control algorithms provide advanced performance for robot manipulator. In this paper an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm and controller parameters are detemined by the pole-placement method. Performance of self-tuning adaptive controller is illusrated by the simulation and experiment for a SCARA robot.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.20-27
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• 1994

This paper presents a controller design to coordinate a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme is applied. Since parameters of the robot manipulators such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters works as a disturbance force. To identify the unknown parameters, an improved adaptive control algorithm is directly derived from a chosen Lyapunov's function candidate based on the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing forces coming from the difference between the actual and the estimated system parameters. Numerical examples are shown using three degree-of-freedom planar arm.