2003 Fiscal Year Final Research Report Summary
On identification of Volterra kernels of nonlinear systems by separating kernel slices
| Project/Area Number |
14550449
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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 |
Control engineering
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| Research Institution | Kumamoto University |
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Principal Investigator |
KASHIWAGI Hiroshi Kumamoto University, Dept. of Mechanical Eng., Professor, 工学部, 教授 (30040380)
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| Co-Investigator(Kenkyū-buntansha) |
HARADA H. Kumamoto University, Dept. of Mechanical Eng., Professor, 工学部, 教授 (90145285)
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| Project Period (FY) |
2002 – 2003
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| Keywords | Nonlinear system / Identification / Volterra kernel / M-sequence / Correlation technique / Model Predictive Control / Process control |
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| Research Abstract |
The authors have recently developed a new method for obtaining Volterra kernels of nonlinear system by use of a pseudorandom M-sequence. In this method, the most important problem is how to separate overlapped kernel slices which appear in the crosscorrelation. The authors have investigated two methods for separating those kernel slices : one is the suitable selection of M-sequence, and the other is amplitude variation method. We have obtained the following results.
(1)On considering the fact that when the amplitude of the input M-sequence is changed, the amplitude of the output from n'th order Volterra kernel varies, we have obtained the method for separating overlapped Volterra kernel slices. The simulation shows that this method is useful.
(2)This method is applied to a chemical process, and the result was presented at AdCONIP held in Kumamoto in June 2002.
(3)This method was also presented at APCCM'2000 held in Dali, China in August 2002.
(4)The general idea of identification of nonlinear system is presented at ICSE held in Coventry, UK, in September 2003.
(5)This method is applied to Nonlinear Model Predictive Control, and the results are presented at ICCAS'03 held in Gyoungju, Korea, in October 2003.
(6)The application of this method to Nonparametric Nonlinear Model Predictive Control appeared in Krean J.Chem.Eng. in 2004
This method is considered to be widely usable in nonlinear industrial control systems.
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