• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1123-1130
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• 2017

This paper deals with the dynamic surface control based tracking control for a drone equipped with a 2-DOF manipulator. First, the dynamics of drone and 2-DOF manipulator are derived separately. And we obtain the combined model of a drone equipped with a manipulator considering the inertia and the reactive torque generated by a manipulator. Second, a dynamic surface control based attitude and altitude control method is presented. Also, multiple sliding mode control based position control method is presented. The system stability and convergence of tracking errors are proven using Lyapunov stability theory. Finally, the simulation results are given to verify the effectiveness of the proposed control method.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.61.2-61
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• 2002

$\textbullet$ Introduction $\textbullet$ Dynamics of robotic manipulator $\textbullet$ Time-varying sliding surface $\textbullet$ Fuzzy rule, Membership function $\textbullet$ Application to a two degree robotic manipulator $\textbullet$ Conclusion

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.10
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• pp.3186-3198
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• 1996

When a robot is to have contact with its enviornment, such as a medi-care robot, it would be advantageous for the robot to have a high compliance. For this reason, a robot having not only a flexible link but also an actuator with compliance, is desirable. This paper is concerned with the position and vibration control of 1 degree of freedom flexible robot using a pneumatic artificial muscle actuator. The dynamics of the manipulator assumed to be and Euler-Bernoulli beam are derived on the basis of the linear mathematical modle. Although this pneumatic artifical muscle actuator has many merits for the compliance robot, it is difficult to make an effective control scheme of this system because of ths nonlinearity and uncertainty on the dynamics of the actuator. By designing a controller using .mu.-synthesis, robust performance against measurement noise, various modeling uncertainties on the dynamics of the servo valve, actuator and mainpulator, is achieved. The effectiveness of the proposed control method is illustrated through simulations and experiments.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.3
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• pp.34-40
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• 2001

Robot manipulator has highly nonlinear dynamics. Therefore the control of multi-link robot arms is a challenging and difficult problem. In this paper a decentralized adaptive fuzzy sliding mode scheme is developed for control of robot manipulators. The proposed scheme does not require an accurate manipulator dynamic model, yet it guarantees asymptotic trajectory tracking despite gross robot parameter variations. Numerical simulation for decentralized control of a 3-axis PUMA arm will also be included.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1013-1016
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• 1995

This paper deals with dynamics and control of a PRP6-DOF parallel manipulator. Dynamic modeling includes the effect of inertia of all links in the mechanism to increase modeling accuracy. Kinematic analysis about forward and inverse kinematics is also explained. Using Lagrange-D' Alambert method we get equations of motions in a link space which fully represent 6DOF motions of the manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.687-693
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• 1989

A robust deterministic control for a class of singularly perturbed uncertain systems, where uncertainties are characterized deterministically rather than stochastically, is developed based mainly on information available on an uncertain reduced-order system. The deterministic control scheme is applied to the motion control of a n degree of freedom robotic manipulator. The parasitic actuator and sensor dynamics of the manipulator are explicitly considered in the stability analysis of the deterministic controller using a singular perturbation model. Simulation and experimental studies for a two degree of freedom, direct drive SCARA manipulator are conducted to evaluate the effectiveness of the derived control scheme.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.9
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• pp.77-83
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• 2003

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 Lyapunov theory. The results of computer simulations show that the robot system is stable, and has excellent trajectory tracking performance.

• 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.

• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.222-230
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• 2012

The paper presents a momentum-based regressor by using Hamiltonian dynamics representation for robotic manipulator. It has an advantage in that the proposed regressor does not require the acceleration measurement for the identification of dynamic parameters. Also, the identification algorithm is newly suggested by solving a minimization problem with constraint. The developed algorithm is easy to implement in real-time. Finally, the effectiveness of the proposed momentum-based regressor and identification method is shown through numerical simulations.

• 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.