SYSTEM IDENTIFICATION OF DYNAMICS OF UNDERWATER VEHICLE USING ARTIFICIAL NEURAL NETWORKS
| Project/Area Number |
05452311
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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 |
海洋工学
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| Research Institution | INSTITUTE OF INDUSTRIAL SCIENCE |
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Principal Investigator |
URA Tamaki University of Tokyo Institute of Industrial Scinece Professor, 生産技術研究所, 教授 (60111564)
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| Co-Investigator(Kenkyū-buntansha) |
FUJII Teruo University of Tokyo Institute of Industrial Science Associate Professor, 生産技術研究所, 助教授 (30251474)
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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 |
¥7,000,000 (Direct Cost: ¥7,000,000)
Fiscal Year 1994: ¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1993: ¥5,000,000 (Direct Cost: ¥5,000,000)
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| Keywords | SYSTEM IDENTIFICATION / UNDERWATER ROBOT / NEURAL NETWORK / UNDERWATER VEHICLE / ADAPTIVE CONTROL / UNMANNED UNTETHERED SUBMERSIBLE / LEARNING / ニューラルネットワーク / 動特性 / シミュレーション / 運動体 / 潜水機 |
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| Research Abstract |
Dynamics of offshore structures, ships, and underwater vehicles such as manned submersibles are complicated and highly nonlinear to be considered with conventional dynamic theories, especially when they are operated in definitely slow speed. Moreover, the dynamics may be changed during operation. In order to deal with such a complex and time varying dynamics, the neural network I/O system is advantageous taking advantage of learning ability even if the input and the output are multiple. In this research, the controller and the identification model consist of the artificial neural network, and the controller is modified adaptively based on the I/O relation of the identification model.
This year, a structure of feed forward neural network and its learning process were proposed to simulate the dynamic behavior of the controlled object. The network includes two kinds of recurrent connections, i.e., from the output layr to the input layr and from the hidden layr to the input layr. The first connection enables the network to obtain the input state variables from its own outputs and the second one is to keep the influence of the past data in itself. In this paper, the learning process is improved to equip the network with the capability of emulating the dynamic behavior including higher-order finite differences. The proposed network is adopted to the neural-network-based control system called "SONCS : Self-Organizing Neural-net Controller System" , which has been developed as an adaptive control system for Underwater Robots. The neural network controller in SONCS can be quickly adapted taking advantage of the network's simulating ability. The efficiency of the network is successfully demonstrated through the application to heading keeping control of a versatile robot called "Twin-Burger".
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Report
(3 results)
Research Products
(24 results)
