Development of a Neural Network Control System for a Micro-Flexible Robotic Arm
| Project/Area Number |
05555071
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| Research Category |
Grant-in-Aid for Developmental Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Dynamics/Control
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| Research Institution | Gifu University |
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Principal Investigator |
SASAKI Minoru Gifu University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (20183379)
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| Co-Investigator(Kenkyū-buntansha) |
TAKAHASHI Kazuhiko Nippon Telegraph and Telephone Corporation, NTT Interdisciplinary Research Labor, NTT境界領域研究所, 研究員
TAKAHASHI Takayuki Tohoku University, Graduate School of Information Sciences, Lecturer, 情報科学研究科, 講師 (70197151)
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| Project Period (FY) |
1993 – 1994
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| Project Status |
Completed (Fiscal Year 1994)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 1994: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1993: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Neural net / Flexible micro-manipulator / Control system / Momentum / Adaptive learning rate / Levenberg-Marquardt optimization |
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| Research Abstract |
A reference signal self-organizing control system using neural networks for flexible micro-manipulators is presented. This control system consists of a series combination of both a plant with a feedback loop and a neural network with a feedforward loop. In this system, the neural network functions as the reference input filter and it organizes a new reference signal to the closed-loop system. Backpropagation can be improved in the two different ways : by heuristics, and by using more powerful methods of optimization. This two new backpropagation methods with improved performance is proposed. By using momentum and an adaptive learning rate training time can be decreased. Momentum decreases backpropagation's sensitivity to small details in the error surface. This helps the network avoid getting stuck in shallow minima which would prevent the network from finding a lower error solution. The use of an adaptive learning rate attempts to keep the learning step size as large as possible while keeping learning stable. The learning rate is made responsive to the complexity of the local error surface. Levenberg-Marquardt optimization makes training times even shorter. Levenberg-Marquardt optimization is more sophisticated method than gradient descent. Experimental results for the trajectory tracking control of flexible micro-manipulators show that proposed control system is effective in accurately following the reference signal.
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Report
(3 results)
Research Products
(17 results)
