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システム/制御/情報 Vol. 14 (2001), No. 2

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. 14 (2001), No. 2

Genetic Algorithmを用いた円形アレーアンテナの給電系の簡略化

大久保 茂

pp. 43-51

抄録

The desired directivities and gains of array antenna with a large number of radiating elements are easy to obtain. By considering array antenna as an aggregate of subarrays, the design, production, inspection and maintenance are simplified, and the reduction in cost can be carried out. Genetic Algorithms are “global” numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. In this paper, circular array antenna is divided into subarrays. Then, the source distribution of circular array antenna can be approximated as a Taylor distribution by combining subarrays having the same source distribution using Genetic Algorithm so that the simplification of feed system becomes possible.

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Genetic Algorithmを用いた円形アレーアンテナの給電系の簡略化

ニューラルネットを用いた移動ロボット車の動的環境における走行制御法

大友 照彦, 小平 実, 大槻 恭士, 大内 隆夫

pp. 52-61

抄録

This paper proposes a more flexible and effective obstacle avoidance travel control system based on the switching of the thought in dynamic environment where many autonomous robot vehiecles move. The proposed system plans various traveling paths by the Cascaded Neural Networks (CNN) which operates by a traffic rule or knowledge, so that robot vehiecle can smoothly move the best suited route by evaluating the energy function value of those paths. To show the effectiveness of the control system, we carried out a computer simulation of the obstacle avoidance by using four vehiecle models, and confirmed that each vehiecle can avoid other vehiecles without falling into the deadlock.

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ニューラルネットを用いた移動ロボット車の動的環境における走行制御法

最小次元観測器に基づく安定化制御器の導出と周波数整形ILQサーボ系設計への応用

中村 恵子, 酒井 雅也, 中島 健一, 藤井 隆雄

pp. 62-70

抄録

In our preceding paper, a design method for an ILQ robust servo system with a full-order obsever and a free parameter was proposed. In this paper, we propose a similar method for an ILQ servo system with a minimal-order observer and a free parameter. First, we derive a stabilizing controller based on a minimal-order observer from a full-order observer-based stabilizing controller by equivalent transformation. Then we apply it to design a frequency-shaping ILQ servo system. Finally, we show a design example for a magnetic levitaion system.

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最小次元観測器に基づく安定化制御器の導出と周波数整形ILQサーボ系設計への応用

ある種の混合整数計画問題への内点法の応用

沢井 一智, 佐伯 修, 辻 毅一郎

pp. 71-77

抄録

Mixed Integer Programming (MIP) problems are widely used. They are generally solved by the branch and bound method. It requires much calculation time to solve these problems. The first relaxed problems of MIP problems occasionally have several optimal basic solutions. In this paper, we propose a method which is an application of the interior point method to the branch and bound method for these problems. It is shown that the proposed method has an advantage over the conventional method. A numerical study on the optimal operation problem of the cogeneration system which is one of MIP problems shows that the proposed method reduces the number of subproblems and the computational time.

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ある種の混合整数計画問題への内点法の応用

実験反復による補償器調整法を用いた2慣性共振系の位置決め制御

浜本 研一, 福田 隆宏, 杉江 俊治

pp. 78-85

抄録

In this paper, we present a two-degree-of-freedom controller tuning for two-mass spring systems with friction based on the IFT (Iterative Feedback Tuning) approach. Existing IFT methods may not work very well for such a system because they heavily rely on the linearity of the plants. In order to cope with such cases, we adopt two strategies. One is the separate tuning of the feedback and feedforward controller. The other is to introduce a quasi-Newton method into a parameter renewal law. Finally, we evaluate its effectiveness by an experiment.

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実験反復による補償器調整法を用いた2慣性共振系の位置決め制御

部分観測マルコフ決定過程における位置ベクトルを用いた強化学習手法の提案

清本 盛明, 亀井 且有

pp. 86-91

抄録

This paper describes a reinforcement learning with a position vector, which does not fall into Partially Observable Markov Decision Process (POMDP). Firstly, a rule structure using the position vector as agent's inside sensory information and a restraint of reward assignment for detours are described and then a new reinforcement learning method composed of them is proposed. Next, the proposed method is compared with a conventional method for relatively simple Partial Observation Markov Environment (POME). As a result, it is shown that the reward assignment to unnecessary rules is restrained, that is, the rewards are given to only effective rules and then an efficient learning is carried out. In addition, we apply the proposed method to the shortest path acquisition problem of POME which can hardly be solved by the conventional method, and obseve that an optimum solution is obtained by the proposed method. Finally, the proposed method is successfully applied to a huge maze used in Japan micro-mouse competition, which shows that the proposed method is effective for such realistic problems.

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部分観測マルコフ決定過程における位置ベクトルを用いた強化学習手法の提案

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