• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.397-400
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• 1991

This paper presents a cartesian space decentralized adaptive controller design for the end effector of the robot manipulator to track the given desired trajectory in the cartesian coordinate. By the cartesian based control scheme, the task related high level motion command is directly executed without solving the complex inverse kinematic equations. The controller does not require the complex manipulator dynamic model, and hence it is computationally very efficient. Each degree of freedom of the end effector on the cartesian space is controlled by a PID feedback controller and a velocity acceleration feed forward conpensation part. Simulation results for a two-link direct drive manipulator conform that the present cartesian based decentralized scheme is feasible.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.1
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• pp.37-43
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• 1996

This paper presents a new control scheme for decoupling the dynamics of two coordinating robot manipulators. A simple full-state feedback scheme with configuration dependent gains can be devised to decouple the system dynamics such that the dynamics of each arm and that of an object held by the two arms is independent of one another. A condition for stability is shown. The advantage of the proposed scheme is that the same control scheme can be applied both for the closed kinematic chain(object-grasping) case and open kinematic chain(no object-grasping) case.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.178-184
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• 2014

Tendon driven robot mechanisms have many advantages such as allowing miniaturization and light-weight designs and/or enhancing flexibility in the design of structures. When designing or analyzing tendon driven mechanisms, it is important to determine how the tendons should be connected and whether the designed mechanism is easily controllable. Graph representation is useful to view and analyze such tendon driven mechanisms that are complicatedly interconnected between mechanical elements. In this paper, we propose a method of generalized graph representation that provides us with an intuitive analysis tool not only for tendon driven manipulators, but also various other kinds of mechanical systems which are combined with tendons. This method leads us to easily obtain structure matrix - which is the one of the most important steps in analyzing tendon driven mechanisms.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.347-350
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• 1992

In this note, we propose a method for designing a robot controller which can suppress the effects of both the model uncertainty and noisy velocity measurements. The controller is an output feedback compensator of which the constant gains are given in terms of a Riccati equation and a Lyapunov equation. The controller guarantees not only uniform boundedness but uniform ultimate boundedness. The stability result is local but the region can be arbitrarily enlarged at the expense of large control gain. The control law needs neither the exact knowledge of the physical robot parameters nor clean velocity measurements.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.937-940
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• 1988

The problem of minimization of energy, consumed by the robot manipulator, is important, notably for larger manipulators, higher working speeds, and loads. Obviously, the stated problem requires the application of optimal control theory, which is being successfully applied for linear system and certain classes of nonlinear systems. However, the application of optimal control theory(in energy or time optimization) leads to substantial practical difficulties, so that significant simplifications are usually performed, either in model complexity or by neglecting the existing constraints. In this paper the problem of obtaining such an optimization method. which would take into account the complete system dynamics and all the constraints is considered. The only method found to be suitable for such a complex optimization should be based on dynamic programming.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.651-653
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• 1998

This thesis propose the prefilter type control strategies using modified chaotic neural networks #or the trajectory control of robotic manipulator. Since the structure of chaotic neural networks and neurons, chaotic neural networks can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis PUMA robot is designed by CNN. The CNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on- line learning and the performance is excellent. The CNN controller have much better controllability and shorter calculation time compared to the RNN controller. Another advantage of the proposed controller could be attached to conventional robot controller without hardware changes.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.8-13
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• 1998

A tracking controller is presented for RLFJ(rigid link flexible joint) robot manipulators with only position measurements. The controller is developed based on the integrator backstepping design method and on the two observers: the first is simple linear form observer for the filtered link velocity errors and the other for the actuator velocities. The proposed controller achieves exponential tracking of link positions and velocities while keeping all internal signals bounded. It also guarantees exponential convergence of the estimated signals to their actual ones. Finally, simulation results are included to demonstrate the tracking performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.11-21
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• 1996

Robot manipulators constituing multi-robot system must exert the desired motion force on an object to preserve tghe fine motion of it. The forces exerte on an object by the end-effectors of multi-inducing force and the internal force. Here, motion-inducing force effects the motion of an object, but internal force as lies in the null space of an object coordinate can't effect it. The motion of an object can't track exactly the desired motion by the effect of an object, but internal force as lies in the null space of the effect of internal force component, therefore internal force component must be considered. In this paper, first, under assumption that we can estimate exactly the parameter of dynamics, we constitute paper, first, under assumption that we can estimate exactly the parameter of dynamics, we constitute the controller concerning internal force. And we obtain the internal force as projecting force sensor readings onto the space spanned by null basis set of jacobian matrix. Using the resolved acceleration control method and the fact that internal force lies in the null space of jacobian matrix, we construct the robust control law to preserve the robustness with respect to the uncertainty of mainpulator parameters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.5
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• pp.486-502
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• 1989

This paper proposes two types of the preview algorithms to predict the velocities and joint positions, and deals with a control approach using the preview algorithms for the precise trajectory control. Specifically, a predictor as the form of discrete time state equations is proposed based on the robot dynamics model linearized by the computed toque method. And another state predictor is proposed by the best line fitting in the least square sense, where present joint velocities and positions and several past positions are employed. Then computer simulations are performed for the SCARA robot with two d.o.f to show the validities of the proposed algorithms.