Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

Zhou, N., Kawano, Y. & Cao, M., 1-Sep-2020, In : IEEE Transactions on Neural Networks and Learning Systems. 31, 9, p. 3696 - 3710 15 p., 8894505.

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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.

Original languageEnglish
Article number8894505
Pages (from-to)3696 - 3710
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number9
Publication statusPublished - 1-Sep-2020

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