Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input SaturationsZhou, 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.
Research output: Contribution to journal › Article › Academic › peer-review
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.
|Pages (from-to)||3696 - 3710|
|Number of pages||15|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Publication status||Published - 1-Sep-2020|