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RISS 인기검색어
- 검색키워드
- 저자 : Nobuhiko Koyama (검색결과 2 건)
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Nobuhiko Koyama,Keisuke Kubota,Ichiro Kitamuki,Masuhiro Nitta,Kiyotaka Kato 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
Several methods have been proposed for designing a controller using experimental data directly without identifying a controlled plant. Among them, optimal controller design methods from one-time experimental data, such as Virtual Reference Feedback Tuning (VRFT), Fictitious Reference Iterative Tuning (FRIT), and Noniterative Correlation-based Tuning (NCbT), are especially expected to reduce the time and cost of designing a controller. VRFT and FRIT make a reference signal from experimental data. In contrast, NCbT is a method that removes noise influence by determining parameters for the controller so as not to have the interrelation of the correlation function between a reference signal and noise. However, VRFT and FRIT cannot deal with data that include noises. Although NCbT can be used to design an optimal controller from experimental data with noise, it is limited to cases where there is no interrelation between a reference signal and noise. In this paper, we show a summary of the conventional method and argue about a problem where there is noise. We also propose a data conversion method using linearity after applying spline fitting instead of using experimental data directly. In addition, we discuss the advantages of the conventional method and the proposed method.
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Comparison of VRFT, NCbT and VRFT with Spline Fitting
Keisuke Kubota,Nobuhiko Koyama,Ichiro Kitamuki,Masuhiro Nitta,Kiyotaka Kato 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
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.
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