| Neural Network-Based Active Control for Offshore Platforms |
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| 作者姓名： | 周亚军 赵德有 |
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| 作者单位： | Department of Naval Architecture，Department of Naval Architecture Dalian University of Technology，Dalian 116024，China，Dalian University of Technology，Dalian 116024，China |
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| 摘 要： | 1 .IntroductionWiththedevelopmentofoceantechnology ,moreandmoreextremelylargeandlongflexibleoff shoreplatformsusedforoilexplorationanddrillingoperationarebuiltinhostileoceanenvironments .Ingeneral,thiskindofplatformsisanonlineardistributedparametersystemanditsnaturalfrequencyfallsclosertothedominantwavefrequencieswiththeincreaseofwaterdepth .Besides ,itsstructureisverycomplexandtheexternalwaveforceontheplatformisuncertain .Thus ,theseplatformsarepronetoexcessivewave inducedoscillationsunderbot…
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Neural Network-Based Active Control for Offshore Platforms |
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| ZHOU Ya-jun and ZHAO De-you.Neural Network-Based Active Control for Offshore Platforms[J].China Ocean Engineering,2003,17(3). |
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| Authors: | ZHOU Ya-jun and ZHAO De-you |
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| Institution: | ZHOU Ya-jun1 and ZHAO De-you Department of Naval Architecture,Dalian University of Technology,Dalian 116024,China |
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| Abstract: | A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network. |
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| Keywords: | active control offshore platform neural network time delay vibration |
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