Motion control and monitoring of robot arm via feedback error learning and statistic learning
| Project/Area Number |
23560531
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | Nara Institute of Science and Technology |
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Principal Investigator |
SUGIMOTO Kenji 奈良先端科学技術大学院大学, 情報科学研究科, 教授 (20179154)
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| Co-Investigator(Kenkyū-buntansha) |
TACHIBANA Takuji 福井大学, 工学部, 准教授 (20415847)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
Fiscal Year 2013: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000)
Fiscal Year 2012: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2011: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
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| Keywords | 制御理論 / 適応学習制御 / フィードフォワード / オンライン同定 / 2自由度構造 / マルチモデル / 非線形システム / ロボットアーム / 学習制御 / 2自由度系 / モデリング / 運動制御 / 逆システム / 統計学習 |
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| Research Abstract |
The objectives of this research are 1) to expand/deepen feedback error learning as a scheme for adaptive learning control, and 2) to examine a statistic learning technique, which has made a lot of progress recently, from a dual viewpoint of feedback error leaning, thereby making full use of the both approaches complementary in order to attain motion control/monitering of robot arms. The former is successful in that seperating control/learning laws has enabled to delete the effect to closed-loop and that generalization to a single filter to a filter bank has lead to a powerful learning control scheme that can treat nonlinearity. On the other hand, concerning the latter, our simulation result has not given what we had expected in advance. We are currently investigating reasons why this does not work well.
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Report
(4 results)
Research Products
(53 results)
