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.
Kazumune HASHIMOTO
Osaka University
Masako KISHIDA
National Institute of Informatics (NII)
Yuichi YOSHIMURA
Osaka University
Toshimitsu USHIO
Osaka University
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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
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@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},}
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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 -