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

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.