• The Plant Pathology Journal
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• 제26권1호
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• pp.17-24
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• 2010

A logistic model for describing combined effects of both temperature and wetness period on appressorium formation was developed using laboratory data on percent appressorium formation of Colletotrichum acutatum. In addition, the possible use of the logistic model for forecasting infection risks was also evaluated as compared with a first-order linear model. A simplified equilibrium model for enzymatic reactions was applied to obtain a temperature function for asymptote parameter (A) of logistic model. For the position (B) and the rate (k) parameters, a reciprocal model was used to calculate the respective temperature functions. The nonlinear logistic model described successfully the response of appressorium formation to the combined effects of temperature and wetness period. Especially the temperature function for asymptote parameter A reflected the response of upper limit of appressorium formation to temperature, which showed the typical temperature response of enzymatic reactions in the cells. By having both temperature and wetness period as independent variables, the nonlinear logistic model can be used to determine the length of wetness periods required for certain levels of appressorium formation under different temperature conditions. The infection model derived from the nonlinear logistic model can be used to calculate infection risks using hourly temperature and wetness period data monitored by automated weather stations in the fields. Compared with the nonlinear infection model, the linear infection model always predicted a shorter wetness period for appressorium formation, and resulted in significantly under- and over-estimation of response at low and high temperatures, respectively.

• 동력기계공학회지
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• 제13권6호
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• pp.43-50
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• 2009

If an internal combustion engine is operated by consolidated control, the minimum fuel consumption is achieved satisfying the demanded objectives. For this, it is necessary that the engine is operated on the ideal operating line which satisfies minimum fuel consumption. In this context of view, there are many tries to achieve given object. However, the parameter in the internal combustion engines are variable and depend on the operating points. Therefore, it is necessary to cope with the uncertainties such that the optimal operating may be possible. From this point of view, this paper gives a controller design method and a robust stability condition for engine torque control which satisfies the given control performance and robust stability in the presence of physical parameter perturbation. Exactly, the present paper considers a robust stability of this 2DOF servosystem with nonlinear type uncertainty in the engine system, and a robust stability condition for the servosystem is introduced. This result guarantees that if the plant uncertainty is in the permissible set defined by the given condition then a gain tuning can be carried out to suppress the influence of the plant uncertainties.

• 한국지진공학회논문집
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• 제10권1호
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• pp.63-74
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• 2006

원전 구조물 및 주요기기의 지진 안전성 평가에서는 내진성능을 정량화하는 방법으로 취약도 분석이 사용되고 있다. 지진취약도 분석은 격납건물의 설계 시 반영된 보수성을 배제한 실질적인 내진성능을 평가하는 것으로 이러한 보수성을 성능 및 응답에 관련된 확률론적 변수로 고려하여 평가하게 된다. 본 연구에서는 비선형 지진 해석으로부터 얻은 구조물의 변위응답을 기초로 한 지진취약도 분석 방법을 제시하였다. 또한 원전부지에서 선정된 발생가능한 근거리지진, 원거리지진, 설계지진 및 확률론적 시나리오지진을 시나리오지진으로 선정하고 이들 지진동에 대한 비선형 지진해석을 통하여 한국 표준형 원전 격납건물의 지진취약도를 평가하였다.

• 제어로봇시스템학회논문지
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• 제7권8호
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• pp.647-655
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• 2001

In adaptive neuro-control, neural networks are used to approximate unknown plant nonlinearities. Until now, most of the studies in the field of controller design for nonlinear system using neural network considers the affine system with fixed number of neurons. This paper considers nonaffine nonlinear systems and on-line variation of the number of neurons. A control law and adaptive laws for neural network weights are established so that the whole system is stable in the sense of Lyapunov. In addition, at the expense of th input, tracking error converges to the arbitrary small neighborhood of the origin. The efficiency of the proposed scheme is shown through simulations ofa simple nonaffine nonlinear system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1949-1950
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• 2006

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2827-2829
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• 2005

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.844-847
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• 1996

This paper describes the servo position control for the 2-axis positioning table the servo controller consists of conventional feedback loops, disturbance observer. To reduce the contour error, which occurs in the multi-dimensions machines, cross-coupled controller(CCC) is suggested. A weak point of the CCC is their low effectiveness in dealing with arbitrary nonlinear contour such as circles and parabolas. This paper introduces a new nonlinear CCC that is based on control gains that vary during the contour movement The gains of CCC and adjusted in real time according to the shape of nonlinear contour. The feedback controller based on the disturbance observer compensated for external disturbance, plant uncertainty and bad effectiveness by friction model. Suggested servo controller which improve the contouring accuracy, apply to the 2-axis system. Simulation results on 2-axis table verify the effectiveness of the proposed servo controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.181-184
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• 1997

This paper propose the systematic procedure of the fuzzy model based controller for the continuous nonlinear system. Fuzzy controller have been successfully applied to many uncertain and complex industrial plants. The design of the fuzzy controller mainly depends on the knowledge from the expert who are familiar with the plant by trial and error. Therefore we need more systematic approach to the design of the fuzzy controller. In this paper, we design fuzzy model based controller applied to the nonlinear system. Unlike the design procedures reported in[8] and[9], we use the nonlinear process directly in designing the controller. This controller has been successfully applied to an inverted pendulum.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2000년도 춘계종합학술대회
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• pp.406-409
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• 2000

본 논문에서는 비선형 시스템의 효과적인 제어를 위해 병렬 연결된 신경회로망 제어방식을 제안한다. 제안한 제어방식은 비선형 시스템을 선형부분과 비선형 부분으로 분리하여 각각에 대해 반복최소자승법과 다층회귀신경회로망을 이용하여 플랜트를 직접 제어하는 방식이다. 제안한 제어방식의 유용성을 확인하기 위해 단일 관절 매니퓰레이터에 적용하여 기존의 다층 신경회로망 제어방식과 비교 검토하여 우수성을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2498-2500
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• 2004

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.