Iterative learning fault diagnosis and fault tolerant control for stochastic repetitive systems with Brownian motion

Lifan Li; Lina Yao; Hong Wang; Zhiwei Gao

doi:10.1016/j.isatra.2021.03.030

Iterative learning fault diagnosis and fault tolerant control for stochastic repetitive systems with Brownian motion

Lifan Li, Lina Yao, Hong Wang, Zhiwei Gao

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

In this paper, the issue of iterative learning fault diagnosis (ILFD) and fault tolerant control (FTC) is studied for stochastic repetitive systems with Brownian motion. Different from existing fault diagnosis (FD) methods, a state/fault simultaneous estimation observer based on iterative learning method is designed. The convergence condition of the ILFD algorithm is given. By employing the fault estimation information, the FTC algorithm is proposed to compensate for the fault effect on the system and to keep the stochastic input-to-state stability of the control system. Finally, the simulation results of an induction motor system and a single-link robotic flexible manipulator system are given to show that the proposed method is validated.

Original language	English
Pages (from-to)	171-179
Number of pages	9
Journal	ISA Transactions
Volume	121
DOIs	https://doi.org/10.1016/j.isatra.2021.03.030
State	Published - Feb 2022
Externally published	Yes

Funding

This work was supported by the NSFC, China grants (No. 61973278, 61374128) and Basic Research Projects of Key Scientific Research Projects of Colleges and Universities in Henan, China21zx007. This work was supported by the NSFC, China grants (No. 61973278 , 61374128 ) and Basic Research Projects of Key Scientific Research Projects of Colleges and Universities in Henan, China 21zx007 .

Keywords

Brownian motion
Fault tolerant control
Iterative learning fault diagnosis
Stochastic repetitive systems

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10.1016/j.isatra.2021.03.030

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abstract = "In this paper, the issue of iterative learning fault diagnosis (ILFD) and fault tolerant control (FTC) is studied for stochastic repetitive systems with Brownian motion. Different from existing fault diagnosis (FD) methods, a state/fault simultaneous estimation observer based on iterative learning method is designed. The convergence condition of the ILFD algorithm is given. By employing the fault estimation information, the FTC algorithm is proposed to compensate for the fault effect on the system and to keep the stochastic input-to-state stability of the control system. Finally, the simulation results of an induction motor system and a single-link robotic flexible manipulator system are given to show that the proposed method is validated.",

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T1 - Iterative learning fault diagnosis and fault tolerant control for stochastic repetitive systems with Brownian motion

AU - Li, Lifan

AU - Yao, Lina

AU - Wang, Hong

AU - Gao, Zhiwei

PY - 2022/2

Y1 - 2022/2

N2 - In this paper, the issue of iterative learning fault diagnosis (ILFD) and fault tolerant control (FTC) is studied for stochastic repetitive systems with Brownian motion. Different from existing fault diagnosis (FD) methods, a state/fault simultaneous estimation observer based on iterative learning method is designed. The convergence condition of the ILFD algorithm is given. By employing the fault estimation information, the FTC algorithm is proposed to compensate for the fault effect on the system and to keep the stochastic input-to-state stability of the control system. Finally, the simulation results of an induction motor system and a single-link robotic flexible manipulator system are given to show that the proposed method is validated.

AB - In this paper, the issue of iterative learning fault diagnosis (ILFD) and fault tolerant control (FTC) is studied for stochastic repetitive systems with Brownian motion. Different from existing fault diagnosis (FD) methods, a state/fault simultaneous estimation observer based on iterative learning method is designed. The convergence condition of the ILFD algorithm is given. By employing the fault estimation information, the FTC algorithm is proposed to compensate for the fault effect on the system and to keep the stochastic input-to-state stability of the control system. Finally, the simulation results of an induction motor system and a single-link robotic flexible manipulator system are given to show that the proposed method is validated.

KW - Brownian motion

KW - Fault tolerant control

KW - Iterative learning fault diagnosis

KW - Stochastic repetitive systems

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Iterative learning fault diagnosis and fault tolerant control for stochastic repetitive systems with Brownian motion

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