Study on the control of the position / force of the manipulator with the neural network model
| Project/Area Number |
02650313
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
船舶構造・建造
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| Research Institution | Kyushu University |
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Principal Investigator |
OGAWARA Yoichi Kyushu University, Faculty of Engineering, Professor, 工学部, 教授 (20214033)
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| Co-Investigator(Kenkyū-buntansha) |
IWAMOTO Seiji Kyushu University, Faculty of Engineering, Assistant, 工学部, 助手 (80091338)
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| Project Period (FY) |
1990 – 1991
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| Project Status |
Completed (Fiscal Year 1991)
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| Budget Amount *help |
¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1991: ¥400,000 (Direct Cost: ¥400,000)
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| Keywords | Ocean Robot Manipulator / Position / Force Control / Neural Network Model / Inverse System / Feedback-Error-Learning Control / Learning Time Constant / Learning Speed / Hybrid Control System / 位置制御 / 小脳適応フィルタモデル / 高速学習方法 |
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| Research Abstract |
The manipulator for the ocean robot is required the ability to adapt its manipulation to various occasion and delicate movement. So some advanced control method are necessary to the control system of the manipulator, and it will be demanded the control of the position of the end effector and the force affected it. Recently, it is paid attention to the application of the neural network. Therefore, in this report, it is represented that the control method of the position of the end effector and the force affected it using the neural network model.
(1) The feed forward controller which uses an adaptive neuro-filter employing the inverse system of dynamics of the manipulator is adopted as an internal model, and the feedback-error-learning scheme is used for the learning process. This control system enables precise operations compared to the conventional feedback control systems, and is adaptable to the change of the character of the plant.
(2) By turning the time constants of the learning, the learning speed which is one of the difficult problem of the neural network is faster than the usual method.
(3) By using present method, it is developed a practical hybrid control method of the position and the force of the manipulator.
From these result, it would be established the foundation of the manipulation technique to the manipulator for the ocean robot.
We are planning the study of the control method to reduce the energy of the manipulation, which considers the particular environment of the ocean robot.
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Report
(3 results)
Research Products
(3 results)
