• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.258-262
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• 1990

Several optimum control algorithms have been proposed to minimize the robot cycle time by velocity scheduling. Most of these algorithms assume that the dynamic and kinematic characteristics of a manipulator are fixed. This paper presents the study of a minimum-time optimum control for robotic manipulators considering parameter changes. A complete set of solutions for parameter identification of the robot dynamics has been developed. The minimum-time control algorithm has been revised to be updated using estimated parameters from measurements.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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• pp.173-181
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• 2004

In normal robot control systems without any actuator failures, it is assumed that actuator torque coefficients applied at each joint have normally 1's all the time. However, it is more practical that actuator torque coefficients applied at each joint are nonlinear time-varying. In other words, it has to be considered that actuators equipped at joints may fail due to hardware or software faults. In this work, actuator torque coefficients are assumed to have non-zero values at all joints. In the case of an actuator torque coefficient which has a zero value at a joint, it means the complete loss of torque on the joint. This paper doesn't deal with the case. As factors of performance degradation of robots, both actuator failures and uncertainties are considered in this paper at the same time. This paper proposes a robust adaptive fault-tolerant control scheme to maintain the required performance and achieve task completion for robot manipulators with performance degradation due to actuator failures and uncertainties. Simulation results are shown to verify the fault tolerance and robustness of the Proposed control scheme.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.53-54
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• 2012

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.251-254
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• 1996

We propose a coordinated motion control algorithm of dual manipulators handling a flexible object. The controller is designed so that it can specify the apparent impedance of the object as well as can control its deformation. The experimental results will illustrate validity of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.179-184
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• 1993

In this paper, an optimal motion control scheme is proposed for robot manipulators. A simple explicit solution to the Hamilton-Jacobi equation is presented. The optimization of motion control is based on the mininization of the torque term affecting the kinetic energy and the augmented error which has the first-order stable dynamics for the position and velocity tracking error. In the presence of parametric uncertainty, an adaptive control scheme using the optimal principle is proposed. The global stability of the closed-loop system is guaranteed by the Lyapunov stability approach, The effectiveness and feasibility of the proposed control schemes are shown by simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.302-305
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• 1996

This paper presents a visual servoing combined by Neural Network with optimal structure and predictive control for robotic manipulators to tracking or grasping of the moving object. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we want to predict the updated position of the object. The Kalman filter is used to estimate the motion parameters, namely the state vector of the moving object in successive image frames, and using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. The validity and effectiveness of the proposed control scheme and predictive control of moving object will be verified by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.441-446
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• 1992

We propose a control architecture for the fuzzy logic control of robot manipulators and a rule base derivation method for a fuzzy logic controller(FLC) using a neural network. The control architecture is composed of FLC and PD(positional Derivative) controller. And a neural network is designed in consideration of the FLC's structure. After the training is finished by BP(Back Propagation) and FEL(Feedback Error Learning) method, the rule base is derived from the neural network and is reduced through two stages - smoothing, logical reduction. Also, we show the performance of the control architecture through the simulation to verify the effectiveness of our proposed method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.32-38
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• 1998

In this paper, we design a robust controller for trajectory control of n-link robot manipulators with bounded parametric uncertainties. The proposed control scheme with integral control improves tracking error due to limit of the robust feedback gains and use of continuous control input for chattering rejection. The integral of the tracking error is augmented to the error system equation and the stability of the closed-loop system is achieved. The performance of the proposed method is demonstrated by simulation on a 2-link manipulator.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.259-262
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• 2001

An improved robust hybrid control law is proposed. This law used the separated bounding function and the stiffness bound. It satisfied the performance though we don't know precise information of contact environments. It guarantees the practical stability in sense of Lyapunov. Simulation was performed to validate this law using a four-axis SCARA type robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.74-74
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• 2000

This paper presents a neural network controller for a rigid-link electrically-driven robot. The proposed controller is designed in conjunction with three neural networks approximating for complicated nonlinear functions. Particularly, the fact, different from conventional schemes, is that the neural network based current observer is used. Therefore, no accurate measurement of the actuator driving current is required. In the proposed controller-observer scheme, the derived weight update rule guarantees the stability of closed-loop system in the sense of Lyapunov. The effectiveness and performance of the proposed method are demonstrated through computer simulation.