Sliding mode based fault-tolerant control of hypersonic reentry vehicle using composite learning

Abstract

A sliding mode based fault-tolerant control method using neural learning is studied for hypersonic reentry vehicle (HRV) in this paper. Based on the non-singular second-order terminal sliding mode, the composite neural learning is adopted to deal with the system uncertainties caused by the additive faults. The main point is to construct the prediction error to evaluate the performance of intelligent approximation. Meanwhile, a disturbance observer is employed to estimate the unknown disturbance while the actuator multiplicative fault is compensated with an adaptive law. The simulation of HRV is conducted to verify the effectiveness of the proposed fault-tolerant control scheme.