• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.209-214
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• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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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.

• 한국지능시스템학회논문지
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• 제7권5호
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• pp.67-76
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• 1997

In this paper, a theoretical study is presented for the force control of a miniature robotic manipulator which is driven by a pair of piezo-electric bimorph cells. In the theoretical analysis, one finger is modeled as a flexible cantilevers with a force sensor at the tip and the finger is a solid beam. The robotic finger is used to hold the objects with different stiffness such as an iron block and a living insect and a moving objcet. So it is very important to develop an adequate controller for the holding operation of the finger. The main problems in force controlling are overdamping, overshoot and unknown environment(such as the stiffness of object and unknown plant parameters). So, the main target is propose the new fuzzy compensation for unknown environment and incease the system performance. The fuzzy compensation is implemented by using PI-type fuzzy approach to identified unknown environment. And the result of proposed controller was compared with the conventaional PID and optimal controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.963-968
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• 1990

An iterative learning control scheme for tracking control of a class of uncertain nonlinear systems is presented. By introducing a model reference adaptive controller in the learning control structure, it is possible to achieve zero tracking of unknown system even when the upperbound of uncertainty in system dynamics is not known apriori. The adaptive controller pull the state of the system to the state of reference model via control gain adaptation at each iteration, while the learning controller attracts the model state to the desired one by synthesizing a suitable control input along with iteration numbers. In the controller role transition from the adaptive to the learning controller takes place in gradually as learning proceeds. Another feature of this control scheme is that robustness to bounded input disturbances is guaranteed by the linear controller in the feedback loop of the learning control scheme. In addition, since the proposed controller does not require any knowledge of the dynamic parameters of the system, it is flexible under uncertain environments. With these facts, computational easiness makes the learning scheme more feasible. Computer simulation results for the dynamic control of a two-axis robot manipulator shows a good performance of the scheme in relatively high speed operation of trajectory tracking.

• KDI Journal of Economic Policy
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• 제12권1호
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• pp.111-125
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• 1990

본고(本稿)는 원화환율(貨換率)의 적정운용(適正運用)을 모색해 보았다. 이를 위해 먼저 Lipschitz(1980)의 방식을 따라 원화(貨)의 실질실효환율(實質實效換率)을 안정시킬 수 있는 적정통화(適正通貨)"바스켓"을 구성해 보았다. 80년대 중반 이후 이를 적용했을 경우 실제의 경우보다 원화(貨)의 실질실효환율(實質實效換率)이 훨씬 안정될 수 있었음이 입증되었다. 또한 특정시점에서 구한 적정가중치(適正加重値)를 계속해서 적용하는 것보다 주요환율(主要換率)과 상대물가간(相對物價間)의 관계변화(關係變化)에 따라 가중치(加重値)를 수정해 나가는 것이 바람직하다는 점도 지적되었다. 이와 같은 적정통화(適正通貨)"바스켓"과 그에 기초한 "바스켓"환율(換率)은 우리의 경상수지(經常收支)나 생산성(生産性) 변화추이(變化推移)와 함께 시장평균환율제도하(市場平均換率制度下)에서 적절한 환율운용(換率運用)을 위한 중장기적(中長期的) 지표(指標)의 하나가 될 수 있을 것이다.