• 대한조선학회논문집
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• 제32권3호
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• pp.37-43
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• 1995

장거리 고속 수중익 쌍동 여객선의 실선 시운전중에 실시한 응력 계측과 분석방법에 대하여 기술하고자 한다. 고속 쌍동선에 설치된 수중익의 중앙부 스트럿부재에 응력 게이지를 부착하여 항해중인 선체에 작용하는 양력과 파랑 변동하중에 의하여 나타나는 수중익 응력을 계측하였으며 이로부터 수중익의 부재력과 수중익에 작용하는 동적하중을 평가하였다. 계측된 수중익 응력의 크기 성분 분포로부터 선박의 예정 취항 해역에서 선박의 수명 동안 나타날 수 있는 최대 기대 응력값과 수중익 양력을 장 단기 해석을 수행하여 추정하였다. 본 계측의 분석에서 채택한 장 단기 해석에서는 서로 유사한 결과를 얻을 수 있었으며 추정된 수중익의 최대 기대 응력값을 수중익 재료의 항복응력과 비교하므로서 본 쌍동선 수중익 구조의 안전성을 검증하고 최대 기대 수중익 양력을 설계에 적용된 하중변수와 비교하므로서 설계 과정에서 채택한 하중 연수의 타당성을 확인하였다. 본 연구에서 제안된 계측 방법과 데이타 분석 과정은 실선계측 자료를 효과적으로 설계에 활용할 수 있게 한다.

• 한국정밀공학회지
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• 제22권8호
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• pp.34-41
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• 2005

Generally, it is not easy to get a suitable controller for multi variable systems. If the modeling equation of the system can be found, it is possible to get LQR control as an optimal solution. This paper suggests an LQR learning method to design LQR controller without the modeling equation. The proposed algorithm uses the same cost function with error and input energy as LQR is used, and the LQR controller is trained to reduce the function. In this training process, the Jacobian matrix that informs the converging direction of the controller Is used. Jacobian means the relationship of output variations for input variations and can be approximately found by the simple experiments. In the simulations of a hydrofoil catamaran with multi variables, it can be confirmed that the training of LQR controller is possible by using the approximate Jacobian matrix instead of the modeling equation and this controller is not worse than the traditional LQR controller.

• 한국정밀공학회지
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• 제23권9호
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• pp.84-91
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• 2006

The synthesis of optimal controllers for multivariable systems usually requires an accurate linear model of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. This paper presents a novel loaming method for the synthesis of LQR controllers that doesn't require explicit modeling of the plant dynamics. This method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the LQR objective function. It becomes easier and more convenient because it is relatively very easy to get the sign of Jacobian instead of its Jacobian. Simulations involving an overhead crane and a hydrofoil catamaran show that the proposed LQR-LC algorithm improves controller performance, even when the Jacobian information is estimated from input-output data.

• 한국정밀공학회지
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• 제20권2호
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• pp.112-119
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• 2003

Generally, PID controller is not suitable to control multi variable system because it is very difficult to tune the PID gains. However, this paper shows that it is not hard to tune the PID gains if we can find a Jacobian matrix of the system. The Jacobian matrix expresses the ratio of output variations according to input variations. It is possible to adjust the input values in order to reduce the output error using the Jacobian. When the colt function is composed of error related terms, the gradient approach can tune the PID gains to minimize the function. In simulation, a hydrofoil catamaran with two inputs and two outputs is applied as a multi variable system. We can easily get the multi variable PID controller by the proposed teaming method. When the controller is compared with LQR controller, the performance is as good as that of LQR controller with a modeling equation.