• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• ICROS
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• v.17 no.4
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• pp.46-50
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• 2011

다변수 입출력 제어시스템에서 제어기를 설계하기 위해서는 단일 입출력 시스템에서와 마찬가지로 저주파수영역에서는 루프전달행렬의 크기가 작은 것이 요구되고, 측정잡음 및 플랜트의 불확실성이 존재하는 고주파수 영역에서는 루프전달행렬의 크기가 작게 되도록하여, 잡음의 영향이 출력에 적게 나타나고, 제어기가 포화되지 않도록 하며, 설계된 제어기가 플랜트 모델의 불확실성을 극복할 수 있게 하는 것이 필요하다. 본 원고에서는 각 전달 행렬들의 특이치의 최대 및 최소값들을 이용하여 다변수 제어기가 갖추어야 할 조건들을 정리하였다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.191-197
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• 2009

In this paper, multi-variable fuzzy model for efficient combustion control of refuse incineration plant is obtained. First, to obtain model of incineration plant which is complex and nonlinear multi-variable fuzzy modeling is performed. Obtained multi-variable fuzzy model predicts outputs of incinerator almost exactly. Then using multi-variable fuzzy model we can build simulator which is used as operation simulator for building of control strategy and training of operator.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.4 no.4
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• pp.61-67
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• 1990

선형 다변수 원자력 발전설비에 있어서 순환펌프 동력변화, 스로틀 밸브 개도등의 변화에 따라 plenum chamber물온도, core inletdhs도, 연료에 미치는 온도등의 응답이 상호간섭을 받지않고 최적제어 될 수 있는 방법을 선형 레귤레이터 기법을 이용해 고찰하였다. 그 결과 순환펌프의 동력변화, 스로틀 밸브 개도의 외란에 대해 plenum chamberanfdhs도, 노심입구 물온도, 연료 온도 변화 등이 적어 비간섭화 된 안정된 제어가 될 수 있음을 나타냈다.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.

• ICROS
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• v.10 no.2
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• pp.19-34
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• 2004

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.8
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• pp.805-815
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• 1992

In this paper, robust decoupling control scheme of miftivarlable systems Is studied. Design methods for Input-Output decoupling systems with robustness against signal failure In arbitrary feedback loop or actuator loop Is suggested based on the Riccati type matrix equation and state feedback, and is simulated In Turbo-Generator systems with B-Input, 2 output. The results of simulation represents the decoupled and stable response against the failure of signal In sensor or actuator loop. However, the system designed by conventional ,it ate feedback shows the unstable response. This method Is applied for robust decoupling control of the complicated multivariable systems.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.14-22
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• 2001

This paper presents identification and control designs using neural networks for a class of multivariable nonlinear servomechanisms. A proposed neuro-controller is a combination of linear controllers and a neural network, and is trained by indirect neuro-control scheme. The proposed neuro-controller is implemented and tested on an IBM PC-based two 2 bar systems holding an object, and is applicable to many de-motor-driven precision multivariable nonlinear servomechanisms. The ideas, algorithm, and experimental results arc described. Moreover, experimental results are shown to be superior to those of conventional control.

• ICROS
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• v.5 no.4
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• pp.14-20
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• 1999