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

• DOI: 10.1049/iet-cta.2017.1309
• Publication Date: 2018-04-11T02:22:15Z
• Authors: Xiao Wang; Jie Guo; Shengjing Tang
• Keywords: 0209 industrial biotechnology; 02 engineering and technology

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.