On stochastic identification and control for micro-tunneling machine
| Project/Area Number |
11650459
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | Science University of Tokyo, Suwa College |
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Principal Investigator |
AIHARA S Science University of Tokyo, Suwa College, Professor, 諏訪短期大学, 教授 (70202455)
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| Project Period (FY) |
1999 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 2001: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2000: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1999: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | System identification / Stochastic Control / Micro-tunneling machine / Stochastic Stability / 確率的安定性 / 境界雑音 / 座靴現象 / ブラウン運動 / マイクロトンネル機械 / 確率的双曲型方程式 / 安定性 / 解の分岐 / 自由境界 |
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| Research Abstract |
During these 3 years, the main research was performed to study the mathematical modeling for the displacement u(t,x) for the micro-tunneling machine. The mathematical model which was constructed in the first year was found that the parameter identification part was not easy and not fitted to the real control situation.
In the second year, we adjusted the mathematical model to fit the real situation. The method used here was to neglect the inertia term. Hence the constructed mathematical model becomes a parabolic-type stochastic system. For such a model, we can find that the buckling phenomena appears with the aid of simulation studies. The main problem is to study the stability conditions in the finite time interval. We also extend the derived stability conditions to fit the case that the micro-tunneling machine to trace the given curved path.
In the third year, we concentrated to estimate the physical parameters in the derived mathematical model. By using the real data, first we try to identify all physical parameters with the aid of maximum likelihood method. However, the observed real data are too noisy and we could not get the reasonable estimates. Then we adopt the value of parameters conjectured in the first year and the small adjustments were estimated by using the maximum likelihood method. This estimation step works well.
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Report
(4 results)
Research Products
(21 results)
