| Project/Area Number |
06650486
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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 |
計測・制御工学
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| Research Institution | Fukuoka Institute of Technology |
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Principal Investigator |
SAGARA Setsuo Fukuoka Institute of Technology Department of Communication and computer Engineering, Professor, 工学部, 教授 (60037679)
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| Co-Investigator(Kenkyū-buntansha) |
江口 三代一 福岡工業大学, 工学部, 助教授 (50176765)
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| Project Period (FY) |
1994 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1996: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1995: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1994: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | nonlinear system / adaptive control / parameter estimation / linear integral filter / polynominal approximation / linearized compensation / stability / modeling error / モデリング / 連続系の同定 |
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| Research Abstract |
The approach for linearized compensated adaptive control of nonlinear systems which has many effective merits such as robastness, stability and accuracy for control is proposed. First, nonlinear system are identified by a second order differential equation with coefficents described second order polynomial. In the case of higher than the second order system, The system can be approcimated by the above nonlinear second order system plus an appropriated linear system. In these system, We can control by using usual linear controller for large range of operationg point on the nonlinear system. However for the large abrupt change of system's parameters, adaptation algonithms sometimes can not work well. In order to improve this defect we choose the linearized stable model before hand for the large amount of parameter changes of nonlinear systems. It is found that the approach can be effective to decrease modelling error by simulating results.
We consider some feed water heating system of a power plant as a practicul application. Its equivalent heat capacity is the most important parameter, it is identified online, then linearized model is constracted.
We use the M-sequence as the test signal for identifing nonlinear system whose amplitude is randomly changed. This signal is quite effective for the system having strong nonlinearity.
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