Multi Car Elevator Control by using Learning Automaton
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- Shiraishi Kazuaki
- Graduate School of Science and Technology, Chiba University
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- Hamagami Tomoki
- Graduate School of Engineering, Yokohama National University
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- Hirata Hironori
- Graduate School of Science and Technology, Chiba University
Bibliographic Information
- Other Title
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- 学習オートマトンによるマルチカーエレベータ制御
- ガクシュウ オートマトン ニ ヨル マルチカーエレベータ セイギョ
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Abstract
We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.
Journal
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- IEEJ Transactions on Industry Applications
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IEEJ Transactions on Industry Applications 125 (1), 91-98, 2005
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390282679635217792
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- NII Article ID
- 10014100146
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- NII Book ID
- AN10012320
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- BIBCODE
- 2005IJTIA.125...91S
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- ISSN
- 13488163
- 09136339
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- NDL BIB ID
- 7206690
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed