| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Outline of Final Research Achievements |
The objective of this research is to apply some techniques of machine learning to control engineering and provide a novel useful tools for control problems in aerospace engineering. To this end, we have provided research results in two different directions. One is to find data-driven algorithms of control theory to cope with data and statistics. The other is to develop practical control methods for aerospace engineering to use such data-driven algorithms. Concerning the former objective, we have proposed modeling and control methods based on Gaussian process regression which is an efficient data-driven estimation tool in machine learning. For the latter objective, we have obtained a robust trajectory planning method for spacecraft and a robust feedback control method for the attitude. Combining those results, we can obtain data-driven control methods for spacecraft.
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