1997 Fiscal Year Final Research Report Summary
STUDY ON CONTINIOUS-TIME SYSTEM IDENTIFICATION IN VIEW OF KNOWLEDGE OF PHYSICAL AND CHEMICAL LAWS
| Project/Area Number |
07650498
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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 | KYUSHU UNIVERSITY |
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Principal Investigator |
WADA Kiyoshi KYUSHU UNIVERSITY DEPARTMENT OF ELECTRICAL AND ELECTRONIC SYSTEMS ENGINEERING,PROFESSOR, 大学院・システム情報科学研究科, 教授 (60125127)
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| Co-Investigator(Kenkyū-buntansha) |
IMAI Jun KYUSHU UNIVERSITY,DEPARTMENT OF ELECTRICAL AND ELECTRONIC SYSTEMS ENGINEERING,RE, 大学院・システム情報科学研究科, 助手 (50243986)
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| Project Period (FY) |
1995 – 1997
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| Keywords | System Identification / Identification of continuous time systems / Patameter Estimation / Sampling Intervals / Prefilters |
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| Research Abstract |
Recently, much attention has been attracted for system identification, stimulated by develepment of robust control theory in the presence of model uncertainty. Considerable improvement of estimation accuracy is expected because processing in smaller sampling period has become possible due to the progress of the technology around microcomputer. We have studied mainly from the viewpoint of
(1) effects of prefileter suce as antialias filter to system identification
(2) development of the method to determine the order of a model
(3) evaluation of system identification methods for robust control
(4) extension to multivariate systems, and
(5) development of methods for nonlinear systems.
Our intention has been development of identification scheme suited well for controller design, from standpoint that the quality of the model depends on that the model has capability to trace adequately the characteristics in proper frequency range, and that prefilter is not only data processing but also ones taking a priori knowledge of the plant into the model. We showed totally in this research, that the identification schemes can be applied to large scale, complex real systems by extending them to multivariate systems and developing nonlinear systems identification, taking account of physical information of the plant.
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