• Journal of the Korean Society for Precision Engineering
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• v.6 no.4
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• pp.108-118
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• 1989

An adaptive control scheme has been recognized as an effective approach for a robot manipulator to track a desired trajectory in spite of the presence of nonliearity and parameter uncertainty in robot dynamics model. In this paper, an adaptive control scheme for a robot manipulator is proposed to design robust controller using model reference adaptive control technique and hyperstability theory but it does away with] assumption that the process is characterized by a linear model remaining time invariant during the adaptation process. The performance of controller is demonstrated by the simulation about position and speed control of a six-link manipulator with disturbance and payload variation.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.308-317
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• 2006

To control the tip position of a flexible-link manipulator, a neural network (NN) controller is proposed in this paper. The dynamics error used to construct NN controller is derived based on output redefinition approach. Without the filtered tracking error, the proposed NN controller can still guarantee the closed-loop system uniformly asymptotically stable as well as NN weights bounded. Furthermore, the tracking error of desired trajectory can converge to zero with the proposed controller. For comparison an NN controller with filtered tracking error is also designed for the flexible-link manipulator. Finally, simulation studies are carried out to verify the theoretic results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.3
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• pp.25-34
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• 1992

This paper proposes a Self-Tuning control algorithm for tracking the reference trajectory by measuring the end-point of robot manipulator whose links are light and flexible, and the performance of it is tested through the computer simulation. As an object of system, a flexible robot manipulator with two-links is considered and an assumed mode shape method including gravity force is adopted to analyze the vibration modes for each links and dynamics equation is derived. The controller is designed as a combined form which consists of dynamic feedforward compensator and self-tuning feedback controller. The one supplies nominal torque and the other supplies variational torque to manipulator. Apart from the, K-incremental predictor is also proposed in order to eliminate the offset error. and it shows that the result of simulation adapted well to load change and rapid velocity.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.100-107
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• 1997

A pneumatic artificial muscle type of actuator, which acts similar to human muscle, is developed recently. In this paper, an adaptive controller is presented for the trajectory tracking problem of a two-degree- of-freedom manipulator using two pairs of pneumatic artificial muscle actuators. Due to the nonlinearity and the uncertainty on the dynamics of the actuator, it is difficult to make the effective control schemes of this system. By the adaptive control law which inclueds a nonlinear "feedforward" term compensating paramet- ric uncertainties in addition to P.I.D. scheme, both golbal stability of the system and convergence of the tracking error are guaranted. The effectiveness of the proposed control method for the manipulator using this actuator is illustrated through experiments.periments.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.17-22
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• 2024

The objective of this work is to design a low cost yet fully functional 4-DOF articulate manipulator for educational applications. The design is based on general purpose, programmable smart servo motors namely the Dynamixel Ax-12. The mechanism for motion was developed by formulating the equations of kinematics and subsequent solutions for joint space variables. The trajectory of end-effector in joint variable space was determined by interpolation of a 3rd order polynomial. The solutions were verified through computer simulations and ultimately implemented on the hardware. Owing to the feedback from the built-in sensors, it is possible to correct the positioning error due to loading effects. The proposed solution offers an efficient and cost-effective platform to study the trajectory planning as well as dynamics of the manipulator.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1633-1642
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• 2000

Like many other mechanical dynamic systems, flexible manipulator systems experience stiction or sticking friction, which may cause input-dependent instabilities. Manipulator performance can be enha nced by identifying friction but it is hard and expensive to measure friction by direct and precise sensing of contact displacements and forces. This study addresses the problem of identifying flexible manipulator joint friction. A dynamic model of a two-link flexible manipulator based upon finite element and Lagrange's method is constructed. The dynamic model includes the effects of joint compliances and actuator dynamics. Friction is also incorporated in the dynamic model to account for stick-slip at the joints. Next, the friction parameters are to be determined. The identification problem is posed as an optimization problem to be solved using nonlinear programming methods. A genetic algorithm is used to increase the convergence rate and the chances of finding the global optimum. The identified friction parameters are experimentally verified and it is expected that the identification technique is applicable to a system parameter identification problem associated with a wide class of nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.263-266
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• 1994

Recently, complicated and dexterous tasks with two or more arms are needed in ninny robot manipulator applications which can not be accomplished with one manipulator. In general, when two arms manipulate an object, tile dynamics of the arms and the object should be considered simultaneously. In order to control the force of tile arms, we can use various control schemes based upon dynamic modeling. But, there are difficulties in solving inverse dynamics equations, and the environment where a manipulator performs various tasks is usually unknown, and we can not describe a model precisely, for instances, the effect of the joint flexibility, and the friction between the arm and the object. Therefore, in this paper, we suggest a new force control method employing fuzzy inference without solving dynamic equations. Fuzzy inference rules and parameters are designed and adjusted with the automatic fuzzy modeling method using the Hough transform and gradient descent method.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.548-551
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• 1997

Performance of force tracking impedance control of robot manipulators is degraded by the uncertainties in the robot and environment dynamic model. The purpose of this paper is to improve the controller robustness by applying neural network. Neural networks are designed to learn the uncertainties in robot and environment model for compensating the uncertainties. The proposed scheme is verified through the simulation of 20DOF robot manipulator.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.175-180
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• 1993

The nonlinear integro-differential equations of motion of a two-link structurally flexible planar manipulator executing contact tasks are presented. The equations of motion are derived using the extended Hamilton's principle and the Galerkin criterion. Also, Models for the wrist-force sensor and impact that occurs when the manipulator's end point makes contact withthe environment are presented. The dynamic models presented can be used to studythe dynamics of the system and to design controllers.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.293-298
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• 1993

In this paper, methods for computing the time-optimal motion of a robotic manipulator are presented that considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacles. The optimization problem can be reduced to a search for the time-optimal path in the n-dimensional position space. These paths are further optimized with a local path optimization to yield a global optimal solution. Time-optimal motion of a robot with an articulated arm is presented as an example.