• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.772-776
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• 1997

For a robot to perfom more versatile tasks, it is invitable for the robot's end-effector to come into contact with its environment. In thos case, to achieve better performance, it is necessary to properly control the contact force between the robot and the environment. In thos work, hybrid control theory is studied and is verified through experiment using a 3 DOF robot. In the experiment, two position/force controllers are used. Fist, proportional-integral-derivative controller is used as the controller for both position and force. Second, computed-torque method is used as the position controller, and proportional-integral-derivative controller is used as the force controller. For a proper modeling used in computed-torque method, the friction torque is measured by experiment, and compensation method is studied. The hybrid control method used in this experiment effectively control the contact force between the end-effector and the environment for various types of jobs.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.24-29
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• 1990

In this paper ,ie propose a hybrid position/force controller of a robot manipulator using double-layer neural network. Each layer is constructed from inverse dynamics and Jacobian transpose matrix, respectively. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained 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 a PUMA 560 manipulator.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.653-660
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• 2000

The automatization by industrial robot of today is merely rely on to the simple position repeating works, but requirements of research and development to the force control which would adapt positively to various restriction or contacting works to environment. In this paper, a learning control algorithm using, neural networks is proposed for the position and force control by a direct-drive robot. The proposed controller is the feedback controller to which the learning function of neural network is added on to and has a character of improving controller's efficiency by learning. The effectiveness of the proposed algorithm is demonstrated by the experiment on the hybrid position and force control of a parallelogram link robot with a force sensor.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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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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• 1997.10a
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• pp.253-256
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• 1997

This paper proposes a control strategy of position and force control in the same direction based on hybrid position/force control. In order to control position and force in the same direction, a weighting matrix is introduced instead of a selection matrix suggested by Raibert and Craig. The major part of the controller output comes from the position controller when a position control error is large, from the force controller when a position control error is large. The proposed algorithm is implemented by the simulation and experiment focusing on the peg-in-hole task where friction exist significantly and is not constant. It also adopts and event control scheme for more efficient performance.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.2
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• pp.232-240
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• 1998

This paper deals with a hybrid position/force control of a robot which is moving on the constrained object with constant force. The proposed controller is composed of a position and force controller. The position controller has a nonlinear compensator which is based on the dynamic robot model and the force controller is attached by feedforward element. A direct drive robot with hard nonlinearity which is controlled by the proposed algorithm has moved on the constrained object with a high stiffness and low stiffness. The results show that the proposed controller has more vibration suppression effects which is occurred to the constrained object with a high stiffness, than a existing feedback controller, and accurate force control can be obtained by comparatively a small feedback gain.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.129.5-129
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• 2001

When a robot manipulator performs some task like grinding or assembling, not only the position control but also the force control of the tools connected to the robot must be controlled. But at this time We were received the uncertainty problems of system information for the force control, for example disturbance, senor resolution and measurement noise. Therefore we proposed fuzzy logic control method instead of existing control theory for the robot manipulator control, for example PID control method. In this paper, We proposed hybrid position/force control of robot manipulator using fuzzy logic control method. To show the validity of the proposed fuzzy controller, We compared fuzzy controller with conventional PID controller.

• Journal of the Semiconductor & Display Technology
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• v.7 no.1
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• pp.17-22
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• 2008

The proposed hybrid controller consists of PID controller, feedforward controller and RLSE (Recursive Least Square Estimating) adaptive controller to compensate the force ripple that is periodic function of position in a linear motor. The modeling of force ripple is divided into the current-dependent and current-independent components. The current independent components never change as the current into the linear motor changes. On the other hand, the current-dependent components change as current varies when the velocity and load of the linear motor change. The proposed controller can compensate both force ripples. The feedforward controller compensates the current-independent components and the RLSE adaptive controller compensates the current-dependents components. We verified the performance of the controller by simulation and experiments.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.1
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• pp.97-103
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• 1995

This paper describes position and force hybrid control for a robot manipulator with artificial rubber muscle actuators. The controller using two control laws such as PID control and fuzzy logic control methods is designed. This paper concludes to show the effectiveness of the proposed controller by some experiments for a two-link manipulator.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.5
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• pp.599-605
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• 2011

A 3-D rehabilitation robot system is developed. The robot system is for the rehabilitation of upper extremities, especially the shoulder and elbow joints, and has 3-D workspace for occupational therapy to recover physical functions in activities of daily living(ADL). The rehabilitation robot system has 1 DOF in horizontal rotational motion and 2 DOF in vertical rotational motion, where all actuators are set on the ground. Parallelogram linkage mechanisms lower the equivalent inertia of the control elements as well as control forces. Also the mechanisms have high mechanical rigidity for the end effector and the handle. In this paper, a hybrid position/force controller is used for controlling positions and forces simultaneously The controller is tuned according to the robot posture. The active motion modes for rehabilitation program consist of active-resisted motion mode and active-free motion mode. The results of the experiments show that the proposed motion modes provide the intended forces effectively.