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Transactions of the Institute of Systems, Control and Information Engineers Vol. 14 (2001), No. 2

ISIJ International
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ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 14 (2001), No. 2

Simplification of Feed System of Circular Array Antenna Using Genetic Algorithm

Shigeru OKUBO

pp. 43-51

Abstract

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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Simplification of Feed System of Circular Array Antenna Using Genetic Algorithm

Travel Control for Robot Vehicle Using Neural Networks in Dynamic Environment

Teruhiko OHTOMO, Minoru KODAIRA, Takashi OTSUKI, Takao OHUCHI

pp. 52-61

Abstract

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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Article Title

Travel Control for Robot Vehicle Using Neural Networks in Dynamic Environment

Derivation of Stabilizing Controller Based on Minimal-Order Observer and Application to Design of Frequency-Shaping ILQ Servo System

Keiko NAKAMURA, Masaya SAKAI, Ken'ichi NAKASHIMA, Takao FUJII

pp. 62-70

Abstract

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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Derivation of Stabilizing Controller Based on Minimal-Order Observer and Application to Design of Frequency-Shaping ILQ Servo System

An Application of the Interior Point Method to some Mixed Integer Programming Problems

Kazunori SAWAI, Osamu SAEKI, Kiichiro TSUJI

pp. 71-77

Abstract

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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An Application of the Interior Point Method to some Mixed Integer Programming Problems

Iterative Feedback Tuning of Controllers for a Two-Mass Spring System with Friction

Kenichi HAMAMOTO, Takahiro FUKUDA, Toshiharu SUGIE

pp. 78-85

Abstract

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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Iterative Feedback Tuning of Controllers for a Two-Mass Spring System with Friction

A New Method for Reinforcement Learning with Position Vector in Partially Observable Markov Decision Process

Moriaki KIYOMOTO, Katsuari KAMEI

pp. 86-91

Abstract

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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Article Title

A New Method for Reinforcement Learning with Position Vector in Partially Observable Markov Decision Process

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