Neural network-based multivariable fixed-time terminal sliding mode control for re-entry vehicles

Xiao Wang; Jie Guo; Shengjing Tang

doi:10.1049/iet-cta.2017.1309

Neural network-based multivariable fixed-time terminal sliding mode control for re-entry vehicles

Xiao Wang, Jie Guo^*, Shengjing Tang

^*Corresponding author for this work

School of Aerospace Engineering

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

24 Citations (Scopus)

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
Pages (from-to)	1763-1772
Number of pages	10
Journal	IET Control Theory and Applications
Volume	12
Issue number	12
DOIs	https://doi.org/10.1049/iet-cta.2017.1309
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

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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.",

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AB - 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.

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Neural network-based multivariable fixed-time terminal sliding mode control for re-entry vehicles

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