2020年9月
Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
- ,
- ,
- 巻
- 31
- 号
- 9
- 開始ページ
- 3696
- 終了ページ
- 3710
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1109/TNNLS.2019.2945920
- 出版者・発行元
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
In this article, we develop attitude tracking control methods for spacecraft as rigid bodies against model uncertainties, external disturbances, subsystem faults/failures, and limited resources. A new intelligent control algorithm is proposed using approximations based on radial basis function neural networks (RBFNNs) and adopting the tunable parameter-based variable structure (TPVS) control techniques. By choosing different adaptation parameters elaborately, a series of control strategies are constructed to handle the challenging effects due to actuator faults/failures and input saturations. With the help of the Lyapunov theory, we show that our proposed methods guarantee both finite-time convergence and fault-tolerance capability of the closed-loop systems. Finally, benefits of the proposed control methods are illustrated through five numerical examples.
- リンク情報
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- DOI
- https://doi.org/10.1109/TNNLS.2019.2945920
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000566342500043&DestApp=WOS_CPL
- URL
- http://xplorestaging.ieee.org/ielx7/5962385/9184294/08894505.pdf?arnumber=8894505
- ID情報
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- DOI : 10.1109/TNNLS.2019.2945920
- ISSN : 2162-237X
- eISSN : 2162-2388
- ORCIDのPut Code : 108109767
- SCOPUS ID : 85090251489
- Web of Science ID : WOS:000566342500043