1997 Fiscal Year Final Research Report Summary
Dynamics Control of Fishing Robots
| Project/Area Number |
07660338
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
農業機械学
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| Research Institution | Tokyo University of Fisheries |
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Principal Investigator |
TODA Masayoshi Tokyo Univ.of Fisheries, Dept.of Marine Science and Technology, assistant professor, 海洋生産学科, 助手 (70262342)
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| Co-Investigator(Kenkyū-buntansha) |
YADA Sadami Tokyo Univ.of Fisheries, Dept.of Marine Science and Technology, professor, 海洋生産学科, 教授
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| Project Period (FY) |
1995 – 1997
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| Keywords | underwater robot / nonlinear system / robust control / linearization / scaled H^* control |
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| Research Abstract |
In the large context of developing a unique manipulator for the fishing and oceanic industry use, in this research term, the control problem considering the dynamics of submerged objects has been considered : we call this manipulator "a fishing robot". In particular, the nonlinear property in such dynamics was focused on, and then the systematic approach to the nonlinear control system synthesis was investigated.
As a nonlinear control approach, the combination of the exact linearization via nonlinear state feedback and linear robust control technique was adopted. When some model is extracted from the real physical plant, there must be some gaps between the model and the real plant such as unmodeled dynamics, parameter errors, and so on. So linearization process must be disturbed by these gaps or the sensor noise. Hence, we assume the incompletely linearized plant to be some linear plant subject to the structured uncertainties, and then, apply the linear robust control technique to it.
Considering the structured uncertainties, the scaled H^* control approach was adopted. In this control problem framework, we has proposed the following approaches,
1) explicit representation of sensor noise as the structured uncertainties,
2) reduction of the structured uncertainty dimension with the consideration of non-correlation among the uncertainties,
3) nonlinear feedback gains with the consideration of the sign of real parameter uncertainties.
To evaluate these approaches, a series of simulations were conducted considering the simple SISO system where polynomial nonlinear terms exist. In these trials, these approach were successful respectively.
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