The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 네트워크 제어 시스템(NCS)을 위한 분산형 이벤트 트리거 메커니즘의 모델 없는 설계를 조사합니다. 이 접근 방식은 컨트롤러와 이벤트 발생 조건에 대한 최적의 매개변수를 동시에 조정하여 규정된 비용 함수를 최소화하는 것을 목표로 합니다. 이 목표를 달성하기 위해 우리는 다음을 사용합니다. 베이지안 최적화 (BO)는 블랙박스 최적화 문제에 대한 최적의 해를 찾기 위한 자동 튜닝 프레임워크로 알려져 있습니다. 전역 최적에 대한 효율적인 검색 전략 덕분에 BO를 사용하면 상대적으로 적은 수의 실험 평가로 이벤트 트리거 메커니즘을 설계할 수 있습니다. 이는 배터리 구동 장치의 제한된 수명과 같은 네트워크 리소스가 제한된 NCS에 특히 적합합니다. 일부 시뮬레이션 사례는 접근 방식의 효율성을 보여줍니다.
Kazumune HASHIMOTO
Osaka University
Masako KISHIDA
National Institute of Informatics (NII)
Yuichi YOSHIMURA
Osaka University
Toshimitsu USHIO
Osaka University
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부
Kazumune HASHIMOTO, Masako KISHIDA, Yuichi YOSHIMURA, Toshimitsu USHIO, "A Bayesian Optimization Approach to Decentralized Event-Triggered Control" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 2, pp. 447-454, February 2021, doi: 10.1587/transfun.2020MAP0007.
Abstract: In this paper, we investigate a model-free design of decentralized event-triggered mechanism for networked control systems (NCSs). The approach aims at simultaneously tuning the optimal parameters for the controller and the event-triggered condition, such that a prescribed cost function can be minimized. To achieve this goal, we employ the Bayesian optimization (BO), which is known to be an automatic tuning framework for finding the optimal solution to the black-box optimization problem. Thanks to its efficient search strategy for the global optimum, the BO allows us to design the event-triggered mechanism with relatively a small number of experimental evaluations. This is particularly suited for NCSs where network resources such as the limited life-time of battery powered devices are limited. Some simulation examples illustrate the effectiveness of the approach.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020MAP0007/_p
부
@ARTICLE{e104-a_2_447,
author={Kazumune HASHIMOTO, Masako KISHIDA, Yuichi YOSHIMURA, Toshimitsu USHIO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Bayesian Optimization Approach to Decentralized Event-Triggered Control},
year={2021},
volume={E104-A},
number={2},
pages={447-454},
abstract={In this paper, we investigate a model-free design of decentralized event-triggered mechanism for networked control systems (NCSs). The approach aims at simultaneously tuning the optimal parameters for the controller and the event-triggered condition, such that a prescribed cost function can be minimized. To achieve this goal, we employ the Bayesian optimization (BO), which is known to be an automatic tuning framework for finding the optimal solution to the black-box optimization problem. Thanks to its efficient search strategy for the global optimum, the BO allows us to design the event-triggered mechanism with relatively a small number of experimental evaluations. This is particularly suited for NCSs where network resources such as the limited life-time of battery powered devices are limited. Some simulation examples illustrate the effectiveness of the approach.},
keywords={},
doi={10.1587/transfun.2020MAP0007},
ISSN={1745-1337},
month={February},}
부
TY - JOUR
TI - A Bayesian Optimization Approach to Decentralized Event-Triggered Control
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 447
EP - 454
AU - Kazumune HASHIMOTO
AU - Masako KISHIDA
AU - Yuichi YOSHIMURA
AU - Toshimitsu USHIO
PY - 2021
DO - 10.1587/transfun.2020MAP0007
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2021
AB - In this paper, we investigate a model-free design of decentralized event-triggered mechanism for networked control systems (NCSs). The approach aims at simultaneously tuning the optimal parameters for the controller and the event-triggered condition, such that a prescribed cost function can be minimized. To achieve this goal, we employ the Bayesian optimization (BO), which is known to be an automatic tuning framework for finding the optimal solution to the black-box optimization problem. Thanks to its efficient search strategy for the global optimum, the BO allows us to design the event-triggered mechanism with relatively a small number of experimental evaluations. This is particularly suited for NCSs where network resources such as the limited life-time of battery powered devices are limited. Some simulation examples illustrate the effectiveness of the approach.
ER -