• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.428-433
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• 1994

This investigates the feasibility of applying fuzzy ogic controllers to the motion tracking control of a direct drive robot manipulator to deal with highly nonlinear and time-varying dynamics associated with robot motion. A fuzzy logic controller with narrow shape of membership functions near zero and wide shape far away zero is analyzed. Simulation and experimental studies have been conducted for a 2 degree of freedom direct drive SCARA robot to evaluate control performances, Fuzzy logic controllers have shown control performances that are often better, or at least, as good as those of conventional PID controllers. Furthermore, the control performance of fuzzy logic controllers can be improved by selecting membership functions of narrow shapes near zero and wide shapes far away zero.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.162.1-162
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• 2001

In this paper, the adaptive fuzzy system is used as an adaptive approximator for robot nonlinear dynamic. A theoretical justification for the adaptive approximator is proving that if the representive point(RP or switching function) and its derivative in sliding mode control are used as the inputs of the adaptive fuzzy system, the adaptive fuzzy system can approximate robot nonlinear dynamics in the neighborhood of the switching surface. Thus the fuzzy controller design is greatly simplified and at the same time, the fuzzy control rule can be obtained easily by the reaching condition. Based on this, a new method for designing an adaptive fuzzy control system based on sliding mode is proposed for the trajectory tracking control of a robot with unknown nonlinear dynamics.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.220-227
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• 1999

The position control accuracy of a robot arm is significantly deteriorated when a long slender arm robot is operated at a high speed. In this case, the robot arm needs to be modeled as a flexible structure, not a rigid one, and its control system needs to be designed with its elastic modes taken into account. In this paper, the vibration control scheme of a one-link flexible manipulator using adaptive input shaper in conjunction with PID controller is presented. The robot consists of a flexible arm manufactured with a thin aluminium plate, an AC servo motor with a harmonic drive for speed reduction, an optical encoder and an accelerometer. On-line identification of the vibration mode is done using the pruned decimation-in-time FFT algorithm to estimate the parameter of the input shaper. Experimental results of the flexible manipulator with a PID controller and input shaper are provided to show the effectiveness of the advocated controllers.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.4
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• pp.123-129
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• 2001

Recent requirements for the fast and accurate motion in industrial robot manipulators need more advanced control tech-niques. To satisfy the requirements, importance of force control is being continuously increased and the expensive force sensor is usually installed to obtain the contact force information in practice. This information is indispensable for the force control of maintaining the desired contact force. However, the sensor cost is too high to be used in industrial applications. In this paper, it is proposed to estimated the contact force occurring between the end-effector of 2 DOF robots and environ-ment. The contact force estimation system is developed based on the static and dynamic models of 2 DOF robot manipula-tors. where the contact force is described with respect to the link torque. The Extended Kalman Filter is designed and its performance is verified in simulations.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.3
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• pp.428-437
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• 2006

This paper attempts to derive and analyze the dynamic system of grasping a rigid object by means of two multi-degrees-of-freedom robot flngers with soft and deformable tips. It is shown firstly that a set of differential equation describing dynamics system of the manipulators and object together with geometric constraint of tight area-contacts is formulated by Lagrange's equation. It is shown secondly that the problems of controlling both the forces of pressing object and the rotation angle of the object under the geometric constraints are discussed. In this paper. the control method for dynamic stable grasping and enhancing dexterity in manipulating things is proposed. It is illustrated by computer simulation that the control system gives the performance improvement in the dynamic stable grasping of the dual fingers robot with soft tips.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.269-274
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• 1990

Usually, robot manipulators in production lines are operated with reperting work trajectories. This paper presents the repeated adaptive learning algorithm for robot manipulates for the case of a trajectory. This algorithm uses the nonlinear dynamic model including the repeated friction compensating term, The advantage of the scheme is that It allows friction compensation which may be otherwise difficult for differently constructed models. A secondary advantage of the sheme is that it can also adapt to torque calculation in order to reduce the computational load of the control computer. To show the efficiency of the proposed controller, a computer simulation is performed for the planar robot manipulator with a 2 degree of freedom.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.61-65
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• 1997

In control of complex system, like robot manipulators, BP neural network have several drawbacks. To overcome this problems, the modified BP neural networks have proposed To find neural network of proper structure for robot manipulator, in this paper, actual experiments using ADSP-21020 for SCARA robot were implemented and have shown the possibility of real-time control and industrial application, without neural chip.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.323-329
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• 1994

In the systems such as walking robots or high speed operating manipulators, the effect of nonlinear terms is important and can not be neglected. Therefore the application of linear control law to such systems is inadequate. Moreover, because of the mathematical modeling errors the systems may become unstable. In this study, we designed a nonlinear controller with sliding mode scheme, which is robust to the modeling errors and applied this control algorithm to the 5 DOF biped robot system. Throught the computer simulations, we examined walking characteris and walking stability of the 5 DOF biped robot system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.14-19
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• 2000

Recent requirements for the fast and accurate motion in industrial robot manipulator need more advanced control techniques. To satisfy the requirements, importance of the force control is being continuously increased and the expensive force sensor is usually installed to obtain the contact force information in practice. This information is indispensable for the force control of maintaining the desired contact force. However the sensor cost is too high to be used in industrial applications. In this paper, it is proposed to estimate the contact force occurred between the end-effector of 2 DOF robots and environment. The contact force estimation system is developed based on the static and dynamic models of 2 DOF robot manipulators, where the contact force is described with respect to the link torque. The Extended Kalman Filter is designed and its performance is verified in simulations.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.9
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• pp.985-996
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• 1990

An inverse kinematic solution of a robot manipulator using multilayer perceptrons is proposed. Neural networks allow the solution of some complex nonlinear equations such as the inverse kinematics of a robot manipulator without the need for its model. However, the back-propagation (BP) learning rule for multilayer perceptrons has the major limitation of being too slow in learning to be practical. In this paper, a new algorithm named Dynamically Reconfiguring BP is proposed to improve its learning speed. It uses a modified version of Kohonen's Self-Organizing Feature Map (SOFM) to partition the input space and for each input point, select a subset of the hidden processing elements or neurons. A subset of the original network results from these selected neuron which learns the desired mapping for this small input region. It is this selective property that accelerates convergence as well as enhances resolution. This network was used to learn the parity function and further, to solve the inverse kinematic problem of a robot manipulator. The results demonstrate faster learning than the BP network.