• 한국정밀공학회지
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• 제17권3호
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• pp.90-101
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• 2000

In this paper, we propose a controller for differentially steered wheeled mobile robots. The controller uses input-output linearization algorithm and artificial neural network to stabilize the dynamic model and compensate uncertainties. The proposed neural network part has 6 inputs, 1 hidden layer, 2 torque outputs and features fast online learning and good performance on structure error learning basis. Simulation results show that the proposed controller perform precisely tracking of reference path and is robust to uncertainties.

• 산업경영시스템학회지
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• 제30권3호
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• pp.82-88
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• 2007

Service engineering is a new discipline which aims at basically understanding of service and a concrete methodology using engineering approach for service design. In this paper is introduced the serviset graph [7] which is a comprehensive concept to represent service products, processes, and operation activities. The proposed concept is a degree of commonality index in a service product in order to deal with service mass customization using the input-output analysis in economic model.

• 대한산업공학회지
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• 제17권2호
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• pp.107-113
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• 1991

This article develops a control model for an open queueing network in terms of both the input and the output processes with stochastic intensities. The input and the output intensities are subject to some capacity limits and optimum control is characterized by a threshold type with a finite upper barrier. A discounted profit is used as a decision criteria, which is revenue minus operating and holding cost.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1711-1716
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• 2004

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial Neural Networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model for control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.471-476
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• 1994

In this paper a new controller is proposed which gives the resultant system the appointed input-output properties, low sensitivity and robust stability. The proposed controller consists of a reference model and a robust compensator. The reference model determines the input-output properties of the total system and is constructed by using the nominal model of the plant. We can design the reference model by applying design techniques which pay attention to steady robustness and no attention to sensitivity and robust stability, and need all state variables of the plant. The robust compensator is obtained as a solution of the mixed sensitivity problem in H infinity control theory. Therefore, low sensitivity and robust stability are guaranteed in the resultant system. The simulation experiments show that the proposed controller is effective and useful.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 춘계종합학술대회
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• pp.404-407
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• 2001

본 논문은 수력터빈시스템에서 동력학 시스템 모델의 매개변수를 얻기 위해 유전알고리즘과 모델 조정기법을 적용하고, 수력터빈시스템으로부터 취득한 실제 운전데이터를 사용하여 계수간을 얻었다. 완성된 모델과 실제시스템의 출력 데이터와 비교하여 유효성을 검증한다. 완성된 모델에 유전알고리즘을 이용하여 제어기의 최적 PID 이득값을 찾을 수 있도륵 하였으며, 디지털 조속기의 적용시에 발생할 수 있는 시행착오를 줄이도록 하는데 목적이 있다고 하겠다.

• Journal of Electrical Engineering and information Science
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• 제2권4호
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• pp.28-36
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• 1997

In this paper, robust stability analysis for the fuzzy feedback linearization regulator is presented. Well-known Takagi-Sugeno fuzzy model is used as the MISO nonlinear plant model. Uncertainty and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainty and disturbances, robust stability of the close system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed by using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. The proposed stability analysis is illustrated by a simple example.

• 대한의용생체공학회:의공학회지
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• 제17권3호
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• pp.395-401
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• 1996

In this paper a new neural network model, time delayed counterpropagation neural networks (TDCPN) which have high recognition rate and short total learning time, is proposed for electromyogram(EMG) recognition. Signals the proposed model increases the recognition rates after learned the regional temporal correlation of patterns using time delay properties in input layer, and decreases the learning time by using winner-takes-all learning rule. The ouotar learning rule is put at the output layer so that the input pattern is able to map a desired output. We test the performance of this model with EMG signals collected from a normal subject. Experimental results show that the recognition rates of the suggested model is better and the learning time is shorter than those of TDNN and CPN.

• 대한기계학회논문집A
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• 제22권2호
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• pp.451-457
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• 1998

This paper describes a modelling method for SCR(selective catalystic reduction) system in refuse incineration plant. We consider the SCR system as a single input and single output system. For modelling the SCR system, an auto regressive exogeneous(ARX) modelling method is used. In this case, we should design the white noise input for modelling and put it on the system as an input$(NH_3)$, and take an outlet NOx as an output. From these two relations, we design the ARX model with 45 second delay time and transform to a discrete system with sampling time of 0.5 second. Using the obtained SCR model, we verify that the outlet NOx is deeply related with stoker`s moving in boiler of refuse incineration plant.

• Journal of information and communication convergence engineering
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• 제6권3호
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• pp.353-357
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• 2008

One objective in developing the next generation of wireless communication systems is to increase data rates and reliability. A promising way to achieve this is to combine multiple-input and multiple-output signal processing with a space-time coding scheme, which offers higher coding and diversity gains and improves the spectrum efficiency and reliability of a wireless communication system. It is noted, however, that time delay differences and phase differences among different channels increase symbol interference and degrade system performance. In this letter, we investigate phase differences and their effects on multiple-input and multiple-output systems, and propose a compensation algorithm for the Rayleigh fading model to minimize their effects.