Abstract
This study develops a neural network (NN)-based multivariable fixed-time terminal sliding mode control (MFTTSMC) strategy for re-entry vehicles (RVs) with uncertainties. A coupled MFTTSMC scheme is designed for the attitude system on the basis of feedback linearisation. A saturation function is introduced to avoid the singularity problem. Adaptive NNs are employed to approximate the uncertainties in RVs, thus alleviating chattering without sacrificing robustness. The whole closed-loop system is proven to be bounded and tracking errors are fixed-time stable. Simulations verify the effectiveness of the proposed strategy.
Original language | English |
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Pages (from-to) | 1763-1772 |
Number of pages | 10 |
Journal | IET Control Theory and Applications |
Volume | 12 |
Issue number | 12 |
DOIs | |
Publication status | Published - 14 Aug 2018 |
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Wang, X., Guo, J., & Tang, S. (2018). Neural network-based multivariable fixed-time terminal sliding mode control for re-entry vehicles. IET Control Theory and Applications, 12(12), 1763-1772. https://doi.org/10.1049/iet-cta.2017.1309