Control system design method of nonlinear mechanical systems based on rational representation
| Project/Area Number |
25630080
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Dynamics/Control
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| Research Institution | Toyota Technological Institute |
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Principal Investigator |
Narikiyo Tatsuo 豊田工業大学, 工学(系)研究科(研究院), 教授 (70231496)
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| Project Period (FY) |
2013-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2015: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 非線形制御理論 / 最適化 |
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| Outline of Final Research Achievements |
In this study we develop a new control system design method for nonlinear systems combining the learning theory with control theory. This new approach is applied to the control of mechanical systems and human-machine systems. Since many of these systems are described by rational function representation, the descriptor non-polynomial systems are used to synthesis of stabilizing controller. For descriptor non-polynomial systems we give a stabilizing controller and the domain of attraction(DOA) by means of the Lyapunov function. To do this we derive the stability conditions for estimating the DOA with input magnitude constraints. From these conditions stabilizing controller is obtained by parallel asynchronous particle swarm optimization. The proposed strategy can be easily exploited to search for both the stabilizing controller and optimal estimates of DOA. Usefulness and validity are demonstrated by numerical simulations.
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Report
(4 results)
Research Products
(2 results)
