Attitude control of hypersonic vehicle based on risk assessment and switching strategy

Based on the online risk assessment and a switching strategy, a switching correction control method is proposed, which aims to improve attitude control performance of hypersonic vehicles (HVs) under different risks. The control scheme mainly includes two parts: online risk assessment based on neural network and switching correction control. Based on different correction controllers under multiple parameter biases, a large amount of specific indicator data can be collected from the simulations. Then the BP neural network is employed for offline analysis and training. A risk assessment network can thus be obtained. Based on the online assessment results by using risk assessment network, a switching control strategy is designed. It can switch adaptively among three correction controllers, thereby ensuring better tracking performance for the HVs under different risks. Finally, the feasibility of this scheme is verified through a series of simulations.