Adaptive sliding mode fault-tolerant control for hypersonic vehicle based on radial basis function neural networks

摘要

In this article, an adaptive sliding mode fault-tolerant control scheme is proposed to address the problem of robust and fast attitude tracking for a hypersonic vehicle in the presence of unknown external disturbances, additive fault and partial loss of effectiveness fault. Firstly, the healthy and faulty models of the vehicle are given. Then, a radial basis function neural network is designed to estimate the unknown additive fault, and the adaptive method is applied to deal with the unknown partial loss of effectiveness fault. Combined with the sliding mode control theory, the fault-tolerant controllers are designed for the outer and inner loops of the faulty system, respectively. The adaptive laws are designed to update parameter estimates to implement the inner-loop controller. Closed-loop stability is analysed and simulation results verify the effectiveness of the proposed fault-tolerant control scheme.