• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.493-496
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• 1990

Recently, the robot control systems are required to be more flexible and intelligent in order to execute more complex and delicate tasks. As an approach to such system, a PC-based robot control system is presented in this paper. The axis controllers are independently designed for each joint of robot manipulator and are supervised by a personal computer. Therefore, the almost system program including the control algorithm can be easily developed and modified in the PC's environment. This proposed control system was successfully applied to 4 axis SCARA-type robot and the associated S/W modules were developed. We can construct sensory robot systems by simply connecting the sensing devices to the PC and the study on this now being progressed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1226-1229
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• 1993

This paper proposes a new on-line fuzzy model identification(ONFID) algorithm in which the fuzzy model evaluation stage is incorporated. The fuzzy model evaluation is performed by the fuzzy equality index which is known to be a useful tool to evaluate the performance of the identified fuzzy model. Then the fuzzy model is updated according to the result of the evaluation. Proposed ONFID algorithm can sensibly identify to the system changes. To show the usefulness of the proposed algorithm, it is applied to the fuzzy model identification problem of the gas furnace and the output prediction problem of the flexible joint manipulator which is a nonlinear system.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.8-13
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• 1998

A tracking controller is presented for RLFJ(rigid link flexible joint) robot manipulators with only position measurements. The controller is developed based on the integrator backstepping design method and on the two observers: the first is simple linear form observer for the filtered link velocity errors and the other for the actuator velocities. The proposed controller achieves exponential tracking of link positions and velocities while keeping all internal signals bounded. It also guarantees exponential convergence of the estimated signals to their actual ones. Finally, simulation results are included to demonstrate the tracking performance.