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システム/制御/情報 Vol. 18 (2005), No. 9

ISIJ International
belloff
オンライン版ISSN: 2185-811X
冊子版ISSN: 1342-5668
発行機関: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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システム/制御/情報 Vol. 18 (2005), No. 9

飽和を有する制御系の積分2次性能解析/設計のためのポリトープ的手法の効用と限界

木山 健, 竹中 光嘉

pp. 301-311

抄録

This paper considers a regional quadratic performance analysis and state feedback synthesis method based on a polytopic approach for linear time invariant systems with saturation nonlinearities. In particular, a domain of quadratic performance is defined as a region of initial states in order to satisfy a quadratic performance. In the analysis case, the polytopic approach is theoretically less conservative than both the circle criterion and the existing linear analysis. However, in the synthesis case, the synthesis condition with the polytopic approach is theoretically equivalent to one with the circle criterion or the linear analysis in the meaning of the same achievable domains or quadratic performance levels in spite of obtaining different state feedback controllers respectively. Finally, this paper proposes a synthesis method making use of effectiveness of the polytopic approach.

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飽和を有する制御系の積分2次性能解析/設計のためのポリトープ的手法の効用と限界

離散時間スライディングモード予見繰返しサーボ系の設計

佐藤 匡, 江上 正, 土谷 武士

pp. 312-321

抄録

This paper propose new design method of discrete-time sliding-mode preview reptitive servo systems. First, an augmented error system which contains periodicity signal, is introduced. Next, the construction of an linear slide mode servo system using augmented system is shown. Then, the preview feed forward compensation based on the optimal control theory is applied. This method not only has the advantage of conventional preview repetition servo system, but has the robustness it is ineffective by the conventional method. The computer experimental results on LDM position control system show the effectiveness of proposed design method.

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離散時間スライディングモード予見繰返しサーボ系の設計

ファジィクラスタリングを用いた主成分分析における変量選択とデータマイニングへの応用

本多 克宏, 市橋 秀友

pp. 322-330

抄録

Knowledge discovery in databases (KDD) or data mining involves fitting models to or determining patterns from high dimensional data sets, and extraction of correlation rules plays an important role. This paper proposes a new approach to knowledge discovery with linear model estimation, in which principal component analysis (PCA) is performed by selecting variables. The proposed algorithm is a hybrid of fuzzy clustering and PCA based on lower rank approximation of data matrix, in which the relative responsibilities of the variables are estimated by using possibilistic constraint for memberships. The proposed algorithm is also enhanced to a local PCA model that can be used for data mining by performing both of linear model estimation and stratified sampling.

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ファジィクラスタリングを用いた主成分分析における変量選択とデータマイニングへの応用

あるクラスの状態リセットシステムの不変集合とネットワーク型分散制御

西垣内 秀紀, 山本 茂

pp. 331-339

抄録

We consider a class of discrete-time state-reset systems with a state-reset mechanism, where some elements of the state vector is reset to particular values when the elements exceeds a prescribed threshold. For the state-reset system we derive the state invariant set. Furthermore, as an example of state-reset systems, we also consider a network-type decentralized control system and show that the invariant set of the state estimation error exists.

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あるクラスの状態リセットシステムの不変集合とネットワーク型分散制御

ノイズ除去に用いる新しい4階偏微分方程式の提案とその数値実験的検証

三原 義樹, 佐野 英樹

pp. 340-342

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ノイズ除去に用いる新しい4階偏微分方程式の提案とその数値実験的検証

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