Multiple-fault diagnosis for spacecraft attitude control systems using RBFNN-based observers

Xiang Gui Guo; Meng En Tian; Qing Li; Choon Ki Ahn; Yan Hua Yang

doi:10.1016/j.ast.2020.106195

Multiple-fault diagnosis for spacecraft attitude control systems using RBFNN-based observers

Xiang Gui Guo, Meng En Tian, Qing Li, Choon Ki Ahn, Yan Hua Yang

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

42 Citations (Scopus)

Abstract

In this paper, a novel multiple-fault diagnosis (MFD) scheme using radial basis function neural network (RBFNN)-based observers is presented for a spacecraft attitude control system (ACS) in the presence of external disturbances and nonlinear uncertainties. Based on dynamic and kinematic models, robust fault detection observers (FDOs) are designed to detect the simultaneous occurrence of actuator, gyro, and star sensor faults. Then, a series of RBFNN-based fault isolation observers (FIOs) are designed to decouple the faults of different components completely. This complete decoupling will guarantee that the diagnosis result of one component is not affected by the faults of other components; thus, multiple faults can be diagnosed simultaneously. To improve the accuracy of fault detection and reconstruction, disturbance compensation observers (DCOs) based on the RBFNN are also designed to compensate for the external disturbances. It is worth noting that the developed fault diagnosis scheme can be used to detect and isolate small faults. Finally, simulation results are presented to show the effectiveness and feasibility of the proposed method.

Original language	English
Article number	106195
Journal	Aerospace Science and Technology
Volume	106
DOIs	https://doi.org/10.1016/j.ast.2020.106195
Publication status	Published - 2020 Nov

Bibliographical note

Funding Information:
This work was supported by National Natural Science Foundation of China (Grant No. 61773056 ), Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (Grant No. BK19AE018 ), the Open Project Program of Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology (Grant No. MADTOF2019A02 ), Fundamental Research Funds for the Central Universities of USTB ( 230201606500061 ), the National Key Research and Development Program of China (Grant number 2017YFB1401203 ), the National Natural Science Foundation of China (Grant No. 61873338 , 61673055 , and 61673056 ), Beijing Key Discipline Development Program (No. XK100080537 ), and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. NRF-2020R1A2C1005449 ). In addition, the authors wish to thank Prof. Jianliang Wang for stimulation and fruitful discussions on the subject of the paper.

Publisher Copyright:
© 2020 Elsevier Masson SAS

Keywords

Attitude control system (ACS)
Disturbance compensation observer (DCO)
Fault isolation observer (FIO)
Multiple-fault diagnosis (MFD)
Small fault detection

ASJC Scopus subject areas

Aerospace Engineering

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10.1016/j.ast.2020.106195

Cite this

@article{b096e72c210148098be42014b048ca6e,

title = "Multiple-fault diagnosis for spacecraft attitude control systems using RBFNN-based observers",

abstract = "In this paper, a novel multiple-fault diagnosis (MFD) scheme using radial basis function neural network (RBFNN)-based observers is presented for a spacecraft attitude control system (ACS) in the presence of external disturbances and nonlinear uncertainties. Based on dynamic and kinematic models, robust fault detection observers (FDOs) are designed to detect the simultaneous occurrence of actuator, gyro, and star sensor faults. Then, a series of RBFNN-based fault isolation observers (FIOs) are designed to decouple the faults of different components completely. This complete decoupling will guarantee that the diagnosis result of one component is not affected by the faults of other components; thus, multiple faults can be diagnosed simultaneously. To improve the accuracy of fault detection and reconstruction, disturbance compensation observers (DCOs) based on the RBFNN are also designed to compensate for the external disturbances. It is worth noting that the developed fault diagnosis scheme can be used to detect and isolate small faults. Finally, simulation results are presented to show the effectiveness and feasibility of the proposed method.",

keywords = "Attitude control system (ACS), Disturbance compensation observer (DCO), Fault isolation observer (FIO), Multiple-fault diagnosis (MFD), Small fault detection",

author = "Guo, {Xiang Gui} and Tian, {Meng En} and Qing Li and Ahn, {Choon Ki} and Yang, {Yan Hua}",

note = "Funding Information: This work was supported by National Natural Science Foundation of China (Grant No. 61773056 ), Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (Grant No. BK19AE018 ), the Open Project Program of Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology (Grant No. MADTOF2019A02 ), Fundamental Research Funds for the Central Universities of USTB ( 230201606500061 ), the National Key Research and Development Program of China (Grant number 2017YFB1401203 ), the National Natural Science Foundation of China (Grant No. 61873338 , 61673055 , and 61673056 ), Beijing Key Discipline Development Program (No. XK100080537 ), and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. NRF-2020R1A2C1005449 ). In addition, the authors wish to thank Prof. Jianliang Wang for stimulation and fruitful discussions on the subject of the paper. Publisher Copyright: {\textcopyright} 2020 Elsevier Masson SAS",

year = "2020",

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journal = "Aerospace Science and Technology",

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AU - Tian, Meng En

AU - Li, Qing

AU - Ahn, Choon Ki

AU - Yang, Yan Hua

N1 - Funding Information: This work was supported by National Natural Science Foundation of China (Grant No. 61773056 ), Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (Grant No. BK19AE018 ), the Open Project Program of Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology (Grant No. MADTOF2019A02 ), Fundamental Research Funds for the Central Universities of USTB ( 230201606500061 ), the National Key Research and Development Program of China (Grant number 2017YFB1401203 ), the National Natural Science Foundation of China (Grant No. 61873338 , 61673055 , and 61673056 ), Beijing Key Discipline Development Program (No. XK100080537 ), and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. NRF-2020R1A2C1005449 ). In addition, the authors wish to thank Prof. Jianliang Wang for stimulation and fruitful discussions on the subject of the paper. Publisher Copyright: © 2020 Elsevier Masson SAS

PY - 2020/11

Y1 - 2020/11

N2 - In this paper, a novel multiple-fault diagnosis (MFD) scheme using radial basis function neural network (RBFNN)-based observers is presented for a spacecraft attitude control system (ACS) in the presence of external disturbances and nonlinear uncertainties. Based on dynamic and kinematic models, robust fault detection observers (FDOs) are designed to detect the simultaneous occurrence of actuator, gyro, and star sensor faults. Then, a series of RBFNN-based fault isolation observers (FIOs) are designed to decouple the faults of different components completely. This complete decoupling will guarantee that the diagnosis result of one component is not affected by the faults of other components; thus, multiple faults can be diagnosed simultaneously. To improve the accuracy of fault detection and reconstruction, disturbance compensation observers (DCOs) based on the RBFNN are also designed to compensate for the external disturbances. It is worth noting that the developed fault diagnosis scheme can be used to detect and isolate small faults. Finally, simulation results are presented to show the effectiveness and feasibility of the proposed method.

AB - In this paper, a novel multiple-fault diagnosis (MFD) scheme using radial basis function neural network (RBFNN)-based observers is presented for a spacecraft attitude control system (ACS) in the presence of external disturbances and nonlinear uncertainties. Based on dynamic and kinematic models, robust fault detection observers (FDOs) are designed to detect the simultaneous occurrence of actuator, gyro, and star sensor faults. Then, a series of RBFNN-based fault isolation observers (FIOs) are designed to decouple the faults of different components completely. This complete decoupling will guarantee that the diagnosis result of one component is not affected by the faults of other components; thus, multiple faults can be diagnosed simultaneously. To improve the accuracy of fault detection and reconstruction, disturbance compensation observers (DCOs) based on the RBFNN are also designed to compensate for the external disturbances. It is worth noting that the developed fault diagnosis scheme can be used to detect and isolate small faults. Finally, simulation results are presented to show the effectiveness and feasibility of the proposed method.

KW - Attitude control system (ACS)

KW - Disturbance compensation observer (DCO)

KW - Fault isolation observer (FIO)

KW - Multiple-fault diagnosis (MFD)

KW - Small fault detection

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DO - 10.1016/j.ast.2020.106195

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SN - 1270-9638

VL - 106

JO - Aerospace Science and Technology

JF - Aerospace Science and Technology

M1 - 106195

ER -

Multiple-fault diagnosis for spacecraft attitude control systems using RBFNN-based observers

Abstract

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Keywords

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