• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.113.1-113
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• 2001

The robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The neural network structure presents the bound function and does not need the concave property of the bound function, The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulators.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.492-496
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• 1988

Usually the first three joint variables (major link) and the next three joint variables (minor link) are used to determine the position and the orientation, respectively, of 6 degrees-of-freedom robot manipulators. In this paper, the Jacobians of 20 major links and 6 minor links are calculated to find the positional maneuverability matrices and orientational maneuverability matrices. Then the kinematic characteristics of the major and minor links are examined. Also we gave the measures of maneuverability and the controllability of the links for the figure of merits of robot manipulator design.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.161-164
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• 2000

An improved robust hybrid control law is proposed This law uses the separated bounding function: so uncertainties of each axis does not affect the others. Also, this law uses the separated $\varepsilon$, so we can take different $\varepsilon$ for each axis This law 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.420-420
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• 2000

In this paper, we propose an adaptive controller using RBFN(radial basis function network) for robot manipulators The structure of the proposed controller consists of a RBFN and VSC-1 ike control. RBFN is used in order to approximate かon system, and VSC-like control to guarantee robustness On the basis of the Lyapunov stability theorem, we guarantee the stability for the total system. And the learning law of RBFN is established by the Lyapunov method, Finally, we apply the proposed controller to tracking control for a 2 link SCARA type robot manipulator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1759-1766
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• 2003

This paper proposes a neuro­fuzzy controllers for trajectory tracking control of robot manipulators. The computed torque method is an effective means for trajectory tracking control. However, the tracking performance of this method is severely affected by the uncertainties of robot manipulators. Therefore, the proposed controller is used to compensate the uncertainties of robot manipulators. In the neuro­fuzzy controllers, the number of fuzzy rules used forty­nine. The effectiveness of the proposed controllers is demonstrated by computer simulations using two­link robot manipulator, As a result, it is confirmed that the output of the proposed neuro­fuzzy controllers can efficiently decrease the uncertainties of robot manipulator.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1453-1463
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• 1994

Recently many robot manipulators are used for various areas of industriesand factories. It has been frequently observed that the robot manipulator fails to complete the function when the object changes its original position, Due to the unexpected impacts and vibrations the center and direction of the object would be shifted in many real application. In this study, a visual sensing algorithm for the robot manipulator is proposed. The algorithm consists of two parts : Detection of the object migration and adjustments of the orobot manipulators Tool Coordinate System. The image filtering technique with visual sensor is applied for the first part of the algorithm. The change of illumination intensity indicates the object migration. Once the object migration is detected, the second part of the algorithm calculates the current position of the object. Then it adjusts the robot manipulators Tool Coordinate System. The robot manipulator and the Visual sensor communicate each other using interrupt technique via proposed algorithm. It has been observed that the proposed algorithm reduces the malfunction of a robot manipulator significantly. Thus it can provide better line balance-up of the manufacturing processes and prevent industrial accidents efficiently.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.37-45
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• 1992

For a precise manipulator control in the presence of environmental uncertainties, it has long been recognized that the robot should be controlled in a task-referenced space. In this respect, an effective visual servo control system for robot manipulators based on neural networks is proposed. In the proposed control system, a Backpropagation neural network is used first to learn the mapping relationship between the robot's joint space and the video image space. However, in the real control loop, this network is not used in itself, but its first and second derivatives are used to generate servo commands for the robot. Second, and Adaline neural network is used to identify the approximately linear dynamics of the robot and also to generate the proper joint torque commands. Computer simulation has been performed demonstrating the proposed method's superior performance. Futrhermore, the proposed scheme can be effectively utilized in a robot skill acquisition system where the robot can be taught by watching a human behavioral task.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1609-1612
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• 1997

Kinematically redundantant manipulators have a nimber of potential advantages over nonredundant ones. Questions associated with manipulability measures for (non)redundant manipulators derived by minimum 2-norm solution and minimum infinity-norm solution in unit joint velocity are examined in detail.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.8
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• pp.42-47
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• 2006

In this paper, we propose a sliding mode control with the bound estimation for robot manipulators without requiring exact knowledge of the robot dynamics. For the bound estimation, the upper bound of the uncertain nonlinearities of robot dynamics is represented as a Fredholm integral equation of the first kind and we propose an adaptive scheme which is only dependent on the sliding surface function. Also, we prove the asymptotic stability for the robot systems using two important properties in the robot dynamics: skew-symmetry and positive-definiteness of robot parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.