Reinforcement-learning based fault-tolerant control

Dapeng Zhang; Zhiling Lin; Zhiwei Gao

doi:10.1109/INDIN.2017.8104852

Reinforcement-learning based fault-tolerant control

Dapeng Zhang, Zhiling Lin, Zhiwei Gao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Engineering systems are always subjected to faults or malfunctions due to age or unexpected events, which would degrade the operation performance and even lead to the operation failure-Therefore, there is a strong motivation to develop fault-tolerant control strategy so that the system can operate with tolerated perform ance de ggr ad ation-In this p ap er, a novel approach based on reinforcement leaning is proposed to design a fault-tolerant controller without need of the information on faults-T simulation example.

Original language	English
Title of host publication	Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017
Publisher	IEEE
Pages	671-676
Number of pages	6
ISBN (Electronic)	9781538608371
DOIs	https://doi.org/10.1109/INDIN.2017.8104852
Publication status	Published - 10 Nov 2017
Event	15th IEEE International Conference on Industrial Informatics, INDIN 2017 - Emden, Germany Duration: 24 Jul 2017 → 26 Jul 2017

Publication series

Name	Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017

Conference

Conference	15th IEEE International Conference on Industrial Informatics, INDIN 2017
Country/Territory	Germany
City	Emden
Period	24/07/17 → 26/07/17

Keywords

Fault-tolerant control
performance index
reinforcement learning

Access to Document

10.1109/INDIN.2017.8104852

Cite this

@inproceedings{301a041cd34e44aeb464eec6cc58aa65,

title = "Reinforcement-learning based fault-tolerant control",

abstract = "Engineering systems are always subjected to faults or malfunctions due to age or unexpected events, which would degrade the operation performance and even lead to the operation failure-Therefore, there is a strong motivation to develop fault-tolerant control strategy so that the system can operate with tolerated perform ance de ggr ad ation-In this p ap er, a novel approach based on reinforcement leaning is proposed to design a fault-tolerant controller without need of the information on faults-T simulation example.",

keywords = "Fault-tolerant control, performance index, reinforcement learning",

author = "Dapeng Zhang and Zhiling Lin and Zhiwei Gao",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 15th IEEE International Conference on Industrial Informatics, INDIN 2017 ; Conference date: 24-07-2017 Through 26-07-2017",

year = "2017",

month = nov,

day = "10",

doi = "10.1109/INDIN.2017.8104852",

language = "English",

series = "Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017",

publisher = "IEEE",

pages = "671--676",

booktitle = "Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017",

address = "United States",

}

Zhang, D, Lin, Z & Gao, Z 2017, Reinforcement-learning based fault-tolerant control. in Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017., 8104852, Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017, IEEE, pp. 671-676, 15th IEEE International Conference on Industrial Informatics, INDIN 2017, Emden, Germany, 24/07/17. https://doi.org/10.1109/INDIN.2017.8104852

Reinforcement-learning based fault-tolerant control. / Zhang, Dapeng; Lin, Zhiling; Gao, Zhiwei.
Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017. IEEE, 2017. p. 671-676 8104852 (Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Reinforcement-learning based fault-tolerant control

AU - Zhang, Dapeng

AU - Lin, Zhiling

AU - Gao, Zhiwei

PY - 2017/11/10

Y1 - 2017/11/10

N2 - Engineering systems are always subjected to faults or malfunctions due to age or unexpected events, which would degrade the operation performance and even lead to the operation failure-Therefore, there is a strong motivation to develop fault-tolerant control strategy so that the system can operate with tolerated perform ance de ggr ad ation-In this p ap er, a novel approach based on reinforcement leaning is proposed to design a fault-tolerant controller without need of the information on faults-T simulation example.

AB - Engineering systems are always subjected to faults or malfunctions due to age or unexpected events, which would degrade the operation performance and even lead to the operation failure-Therefore, there is a strong motivation to develop fault-tolerant control strategy so that the system can operate with tolerated perform ance de ggr ad ation-In this p ap er, a novel approach based on reinforcement leaning is proposed to design a fault-tolerant controller without need of the information on faults-T simulation example.

KW - Fault-tolerant control

KW - performance index

KW - reinforcement learning

UR - https://www.scopus.com/pages/publications/85041217813

U2 - 10.1109/INDIN.2017.8104852

DO - 10.1109/INDIN.2017.8104852

M3 - Conference contribution

AN - SCOPUS:85041217813

T3 - Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017

SP - 671

EP - 676

BT - Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017

PB - IEEE

T2 - 15th IEEE International Conference on Industrial Informatics, INDIN 2017

Y2 - 24 July 2017 through 26 July 2017

ER -

Reinforcement-learning based fault-tolerant control

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this