TY - GEN
T1 - Reinforcement-learning based fault-tolerant control
AU - Zhang, Dapeng
AU - Lin, Zhiling
AU - Gao, Zhiwei
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
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 - http://www.scopus.com/inward/record.url?scp=85041217813&partnerID=8YFLogxK
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 - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Industrial Informatics, INDIN 2017
Y2 - 24 July 2017 through 26 July 2017
ER -