1995 Fiscal Year Final Research Report Summary
Study on Measurement and Control of Dynamic Behavior of Subnano-mechanism
| Project/Area Number |
06452168
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
設計工学・機械要素・トライボロジー
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
SHIMOKOHBE Akira Precision and Intelligence Laboratory, Tokyo Institute of Technology, Professor, 精密工学研究所, 教授 (40016796)
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| Co-Investigator(Kenkyū-buntansha) |
SATO Kaiji Precision and Intelligence Laboratory, Tokyo Institute of Technology, Research A, 精密工学研究所, 助手 (00215766)
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| Project Period (FY) |
1994 – 1995
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| Keywords | Positioning / PID control / Fuzzy control / neural network control / Optimal control / Coarse and fine positioning mechanism / Ball screw / Piezoelectric actuator |
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| Research Abstract |
The aim of this research project was to develop the coarse and fine mechanism system with a long working range on the order of 10 millimeters and high positioning resolution for realize more than sub-nanometers for realize sub-nanometer machining and measuring on the order of 10 millimeters. In the project, first, the experimental sub-nanomechanism which consists of the coarse and the fine mechaisms was made and modeled for the aim and then the best fit control methods for the mechanism were discussed. PID,Fuzzy, Neural network and Linear quadratic control method were selected for the discussion of the best fit one. In order to evaluate these control method, the specification of basic control design was determined and the robustness of the positioning systems with their controller were examined theoretically and experimentally. The results were the following ;
(1) The controller based on PID algorithm for the sub-nanometer mechanism was satisfied the specification of the control design and had the good robustness to the change of the step height.
(2) The controller based on Fuzzy algorithm for the mechanism was satisfied the specification and the good robustness to the change of the weight of the movable table and the addition of the constant load.
(3) In case of the controller based on Neural network algorithm, the number and the kind of the weight of the neuron changed by the back propagation algorithm influenced on the performance of the system. In the experiments, the limit of the number of the weight changed is necessary for high precision positioning.
(4) Though the linear quadratic controller designed for the fine positioning mechanism can control the table precisely, the controller for the coarse and fine positioning mechanism causes the vibration of its table near the reference position.
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