• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.669-672
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• 2012

This paper investigates an algorithm for robust fault diagnosis in robot manipulators. The TOSM (Third Order Sliding Mode observer) provides both theoretically exact observation and unknown fault identification without filtration. The EOI (Equivalent Output Injections) of the TOSM observers can be used as residuals for the problem of fault diagnosis and to identify the unknown faults. The obtained fault information can be used for fault detection, isolation as well as fault accommodation to the self-correcting failure system. The computer simulation results for a PUMA 560 robot are shown to verify the effectiveness of the proposed strategy.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.414-427
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• 1990

This paper presents the development of the graphic simulator for an assembly workcell. The assembly workcell consists of two PUMA 560 manipulators, a conveyor belt system, a work table, and a vision sensor. In this study, the Petri Net theory is applied to model the assembly workcell and to construct the simulator. The event scheduling approach is used to simulate the cell. In order to show the graphic display of the simulation process, robot modelling, component modelling, and world modelling are included. The developed simulator is used to display the transition of the system state during the simulation. It is also used as a tool in selection the best resource states by studying the performance of the system as the resource states are changed.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.816-824
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• 1991

It is well known that dynamic control is needed for fast and accurate control. Neural networks are ideal for representing the strongly nonlinear relationship in the dynamic equations including complex unmodeled effects. It thus creates many advantages over conventional methods such as simple, fast and accurate control through neural network's inherent learning and massive parallelism. In this paper, dynamic control of the full six degrees of freedom of an industrial robot arm will be presented using neural networks. Moreover, through application to a real robot the usefulness of neurocontrol is demonstrated. The back propagation and feedback-error learning is used to train the neurocontroller. Simulated control of a PUMA 560 arm demonstrates that it moves at high speed with good accuracy and generalizes over untrained trajectories as well as adapt to unforseen load changes and sensor noise.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.280-285
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• 1991

Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, a learning control architecture for the dynamic control of a robot manipulator is developed using inverse dynamic neurocontroller and linear neurocontroher. The inverse dynamic neurocontrouer consists of a MLP (multi-layer perceptron) and the linear neurocontroller consists of SLPs (single layer perceptron). Compared with the previous type of neurocontroller which is using an inverse dynamic neurocontroller and a fixed PD gain controller, proposed architecture shows the superior performance over the previous type of neurocontroller because linear neurocontroller can adapt its gain according to the applied task. This superior performance is tested and verified through the control of PUMA 560. Without any knowledge on the dynamic model, its parameters of a robot , (The robot is treated as a complete black box), the neurocontroller, through practice, gradually and implicitly learns the robot's dynamic properties which is essential for fast and accurate control.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.969-972
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• 1988

A conventional robotic manipulator is usually a very complicated system whose dynamics is too computationally time consuming for dynamic analysis and real time control. The authors have proposed the general design criteria of the robot links which greatly simplify the robot dynamic characteristics. In this paper these design guidelines are applied to a 6 degree of freedom PUMA 560 robot in order to realize actual implementation of the design concept. Based upon the design concept, the dynamic equations of the redesigned robot were derived. Dynamic characteristics of two systems, the ideally designed and conventional robot, are compared with respect to the joint input torque characteristics and degree of the coupling between the robot joints.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.866-871
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• 1991

The multi-layer neural network possesses the desirable characteristics of parallel distributed processing and learning capacity, by which the uncertain variation of the parameters in the dynamically complex system can be handled adoptively. However the error back propagation algorithm that has been utilized popularly in the learning procedure of the mulfi-Jayer neural network has the significant limitations in the real application because of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manipulator.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.1
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• pp.8-17
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• 1988

This paper describes a robot simulator which enables a user to model a robot geometrically, and to evaluate performances of various robot control algorithms as well as to obtain physical understanding of robot and acruator dynamics. To achieve these goals, the kinematics and dynamics of a robot and interactive 3-D computer graphics which visulaize the motion of the robot were studied. The developed robot simulator consists of two parts:a dynamic simulator and a graphic simulator. To evaluate the robot simulator PUMA-560, Stanford arm, and RHINO robot were considered and a DG MV/10000 super mini-computer and an IBM-PC/XT personal computer were used.

• Korean Journal of Computational Design and Engineering
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• v.4 no.1
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• pp.12-18
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• 1999

A deformable non-Uniform Rational B-Spline (NURBS) based volume is programed for the force reflecting exoskeleton haptic device. In this work, a direct free form deformation (DFFD) technique is applied for the realistic manipulation. In order to implement the real-time deformation, a nodal mapping technique is used to connect points on the virtual object with the NURBS volume. This geometric modeling technique is ideally incorporated with the force reflecting haptic device as a virtual interface. The results in this work introduce details for the complete set-up for the realistic virtual clay modeling task with force feedback. The force reflecting exoskeleton haptic manipulator, coupled with a supporting PUMA 560 manipulator and the virtual clay model are integrated with the graphics display, and results show that the force feedback from the realistic physically based virtual environment can greately enhance the sense of immersion.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.130-134
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• 1992

The ability of a robot system to comply to an environment via the control of tool-environment interaction force is of vital for the successful task accomplishment in many robot application. This paper presents the implementation of external force control for two dimensional contour following task using a commercial robot system. Force accommodation is used since a constraint imposed in our work is not to modify the commercial robot system. A linear, decoupled model of two dimensional contour following system in the discrete time domain is derived first. Then the experimental verification of linear control is obtained using a PUMA 560 manipulator with standard Unimation controller, Astek FS6-120A six axis wrist force sensor attached externally to the arm and LSI-11173 microcomputer. Experimentally obtained data shows that the RMS contact force error is 0.8246 N when following the straight edge and 2.3768 N when following 40 mm radius curved contour.