지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2012.10
- 수록면
- 333 - 336 (4page)
이용수
초록· 키워드
오류제보하기
In order to design an optimal controller for a feedback system with an unknown plant, it is a common way to identify the plant by performing plural experiments. On the contrary, certain methods used to design a controller using the data from one-time experiments have been proposed. Such methods include Virtual Reference Feedback Tuning (VRFT) and Noniterative Correlation-based Tuning (NCbT). These methods are expected to reduce the design cost. However, VRFT cannot be used to design an optimal controller from a data with noise and NCbT is limited in that there is no correlation of a reference signal with noise. An actual plant often has higher harmonics of an input signal as noise. Such noises are correlated with a reference signal. Therefore, a controller design must be able to deal with various kinds of noises. This paper proposes a method of applying spline fitting to VRFT. To verify the effectiveness of this technique, we compared the response of the proposed method with that of VRFT and NCbT on a simulator. Then, we designed each controller using VRFT, NCbT, and VRFT+SF, respectively, by providing white Gaussian noise and periodic noise. As a result, this paper shows that the proposed method surpasses the original VRFT and NCbT based on overall experimental results.
목차
Abstract
1. INTRODUCTION
2. APPROACHES
3. VRFT with SPLINE FITTING
4. EXPERIMENT
5. DISCUSSION
6. CONCLUSIONS
REFERENCES
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