• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.636-643
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• 1998

This paper is presents multivariable control scheme for industrial robot manipulators. The control scheme consists of two loops. The modeling error between linearized robot model and actual robot model is compensated in error compensation loop. The PID control loop is designed for pole assignment to stability of robot system and utilized for trajectory tracking. Alternatively computer simulation results are given for illustration purpose of suggested controller.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.524-529
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• 2005

In this paper, a novel neural network compensation technique for PD like fuzzy controlled robot manipulators is presented. A standard PD-like fuzzy controller is designed and used as a main controller for controlling robot manipulators. A neural network controller is added to the reference trajectories to modify input error space so that the system is robust to any change in system parameter variations. It forms a neural-fuzzy control structure and used to compensate for nonlinear effects. The ultimate goal is same as that of the neuro-fuzzy control structure, but this proposed technique modifies the input error not the fuzzy rules. The proposed scheme is tested to control the position of the 3 degrees-of-freedom rotary robot manipulator. Performances are compared with that of other neural network control structure known as the feedback error learning structure that compensates at the control input level.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1160-1166
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• 2015

This paper proposes a decentralized robust adaptive control scheme for robot manipulators with input torque saturation in the presence of uncertainties. The control system should consider the practical problems that the controller gain coefficients of each joint may be nonlinear time-varying and the input torques applied at each joint are saturated. The proposed robot controller overcomes the various uncertainties and the input saturation problem. The proposed controller is comparatively simple and has no robot model parameters. The proposed controller is adjusted by the adaptation laws and the stability of the control system is guaranteed by the Lyapunov function analysis. Simulation results show the validity and robustness of the proposed control scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.259-262
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• 1997

This paper presents a visual servoing of RV-M2 robot manipulators to track and grasp moving object, using pruned dynamic recurrent neural networks(DRNN). The object is stationary in the robot work space and the robot is tracking and grasping the object by using CCD camera mounted on the end-effector. In order to optimize the structure of DRNN, we decide the node whether delete or add, by mutation probability, first in case of delete node, the node which have minimum sum of input weight is actually deleted, and then in case of add node, the weight is connected according to the number of case which added node can reach the other nodes. Using evolutionary programming(EP) that search the struture and weight of the DRNN, and evolution strategies(ES) which train the weight of neuron, we pruned the net structure of DRNN. We applied the DRNN to the Visual Servoing of a robot manipulators to control position and orientation of end-effector, and the validity and effectiveness of the pro osed control scheme will be verified by computer simulations.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.12
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• pp.106-113
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• 1996

This paper presents a modeling of undersea robot manipulators and a control scheme appropriate for manipulating the manipulators working under the unstrcuctured sea water environment. Under the sea, the added mass and added moment of inertia, buoyancy, and drag forces should be considered in modeling the dynamics of the robot manipulators. Due to the complexity of them, the desired dynamics of manipulators can not be accomplished by the conventional control schemes. Hence, a sliding mode control is applied to control the modeling error.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.211-218
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• 1999

The traditional robot manipulators are actuated by continuous range of motion actuators such as motors or hydraulic cylinders. However, there are many applications of mechanisms and robotic manipulators where only a finite number of locations need to be reached, and the robot’s trajectory is not important as long as it is bounded. Binary manipulator uses actuators which have only two stable states. As a result, binary manipulators have a finite number of states. The number of states of a binary manipulator grows exponentially with the number of actuators. This kind of robot manipulator has some advantage compared to a traditional one. Feedback control is not required, task repeatability can be very high, and finite state actuators are generally inexpensive. And this kind of robot manipulator has a fault tolerant mechanism because of kinematic redundancy. In this paper, we solve the inverse kinematic problem of a binary parallel robot manipulator using neural network and test the validity of this structure using some arbitrary points m the workspace of the robot manipulator. As a result, we can show that the neural network can find the nearest feasible points and corresponding binary states of the joints of the robot manipulator

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1271-1279
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• 2017

A highly accurate model-free controller is proposed for trajectory tracking control of robot manipulators. The proposed controller incorporates time-delay estimation (TDE) to estimate and cancel continuous nonlinearities of robot dynamics, and exploits fuzzy logic systems to suppress the effect of the TDE error, which is due to discontinuous nonlinearities such as friction. To this end, integral sliding mode is defined using desired error dynamics, and a Mamdani-type fuzzy inference system is constructed. As a result, the proposed controller achieves the desired error dynamics well. Implementation of the proposed controller is easy because the design of the controller is intuitive and straightforward, and calculations of the complex robot dynamics are not required. The tracking performance of the proposed controller is verified experimentally using a 3-degree of freedom PUMA-type robot manipulator.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.837-839
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• 1995

This paper reports the current state of the intelligent mobile robot with flexible manipulators. Several technologies that have been developed and/or being developed are presented in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.731-737
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• 2013

In this paper, we propose an image-based output feedback robust controller of robot manipulators which have bounded parametric uncertainty. The proposed controller contains an integral action and high-gain observer in order to improve steady state error of joint position and performance deterioration due to measurement errors of joint velocity. The stability of the closed-loop system is proved by Lyapunov approach. The performance of the proposed method is demonstrated by simulations on a 5-link robot manipulators with two degrees of freedom.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.454-457
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• 2002

This paper presents a new motion control for singularity avoidance in 6 DOF articulated robot manipulators, based on a speed limiting algorithm for joint positions and velocities. For a given task, the robot is controlled so that the joints move with acceptable velocities and positions within the reachable range of each joint by considering the velocity limit. The proposed method was verified using MATLAB-based simulations.