• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.486-489
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• 1996

The objective of this paper is to design a force feedback controller for bilateral control of a master-slave manipulator system. In a bilateral control system, the motion of the master device is followed by the slave one, while the force applied to the slave is reflected on the master. In this paper, a fuzzy logic controllers applied to the system. Using the fuzzy logic controller, the knowledge of the system dynamics is not needed. Simulations and experimental results show the performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1324-1327
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• 1996

This paper presents an open architecture controller (OAC) for machining systems and describes the OAC testbed at Seoul National University. Because our OAC is designed for fully open systems, it does not depend on any specific hardware or software components. This openness includes software reusability which enables integration of a wide range of monitoring and control features. In addition to openness, our OAC system provides guaranteed real-time performance, an important requirement for advanced manufacturing.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.981-986
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• 2000

A pulse amplitude modulation(PAM) controller is designed for the 3 axis attitude control of a sounding rocket. a certain number of fixed level of thrust are used for the pulse amplitude modulation and the nonlinearity of the controller is considered to examine the existence of the limit cycles and the stability analysis is carried out with the aid of Nyquist plot.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.856-860
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• 1991

This paper proposes a modified fuzzy controller using a neural network. This controller can automatically identify expert's control rules and tune membership functions utilizing expert's control data. Identificaton capability of the fuzzy controller is examined using simple numerical data. The results show that the network in this paper can identify nonlinear systems more precisely than conventional fuzzy controller using neural network.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.160-165
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• 1989

This paper deals with the robust controller design of robotic manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach followed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with assumption that process is characterized by a linear model remaining time invariant during adaptation process. A computer simulation has been performed to demonstrate the performance of the designed control system in task coordinates for stanford manipulator with payload and disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.878-883
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• 1991

In this paper, we propose the fuzzy-neural controller which is fuzzy controller with learning ability of neural network. The neural network in this controller is same as the membership function in current fuzzy controller and a parts of inference rules. And, it can be easily extend the control algorithm to multivariable systems. We can show effectiveness of the control algorithm through experiment of the inverted pendulum system.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.638-641
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• 1992

In this paper, a hybrid position/force control scheme is proposed. The control scheme modifies the position command for force control against constraint surface of environment and is very simply designed and implemented. The merits of the control scheme are that it can cope with change of constraint conditions and small position inaccuracy of the environment. A constraint surface position observer is also proposed to reduce disturbances on controlled force.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.230-235
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• 1992

We propose a method of generating data to train a neural network controller. The data can be prepared directly by an iterative learning technique which repeatedly adjusts the control input to improve the tracking quality of the desired trajectory. Instead of storing control input data in memory as in iterative learning control, the neural network stores the mapping between the control input and the desired output. We apply this concept to the trajectory control of a two link robot manipulator with a feedforward neural network controller and a feedback linear controller. Simulation results show good generalization of the neural network controller.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.496-501
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• 1992

In this paper, the optimal regulating algorithm are developed by adjusting the scaling - factor and estimating the linguistic control rule according to the results which are attaining from the first defined parameters, and the fuzzy level controller is realized usign the algorithm. The proposed controller apply to the fuzzied level control process, the cement grinding plant. The results shows that the control performance is greatly improved.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.112-116
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• 1992

In this paper, a fuzzy logic controller(FLC) is designed for the pointing loop of the spin-stabilized platform. For the fuzzy inference, a fuzzy accelerator board using the Togai InfraLogic software and digital fuzzy processor(DFP110FC) is designed, and a validation of an algorithm for fuzzy logic control is also presented. The pointing loop of the spin-stabilized platform using FLC has better performance of step responses than a proportional controller in case of same loop hain through the software simulation and the experiment of implemented hardware.