Neural control of hypersonic flight dynamics with actuator fault and constraint  

作　　者：WANG ShiXing ZHANG Yu JIN YuQiang ZHANG YongQuan 

机构地区：[1]Department of Control Engineering, Naval Aeronautical and Astronautical University [2]Department of Computer Science and Technology, Tsinghua University [3]School of Aeronautics and Astronautics, Zhejiang University [4]Systems Engineering Research Institute, China State Shipbuilding Corporation

出　　处：《Science China(Information Sciences)》2015年第7期68-77,共10页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.61273058,61005085);National High-tech R&D Program of China(863 Program)(Grant No.2014AA7054018);Aeronautical Science Foundation of China(Grant No.20145184009);Fundamental Research Funds for the Central Universities(Grant No.2012QNA4024)

摘　　要：This paper deals with the control problem of actuator fault and saturation for hypersonic flight vehicles. Different from previous back-stepping design, the scheme is on transforming the dynamics into the ＂prediction function＂. The controller is constructed with high gain observer, while the effect of fault and saturation is compensated by neural networks. For the input saturation, the auxiliary dynamics is included to design the adaptive learning law. The neural weights and filtered tracking error are guaranteed to be bounded via Lyapunov approach. The effectiveness of the proposed method is verified by simulation of winged-cone model.This paper deals with the control problem of actuator fault and saturation for hypersonic flight vehicles. Different from previous back-stepping design, the scheme is on transforming the dynamics into the ＂prediction function＂. The controller is constructed with high gain observer, while the effect of fault and saturation is compensated by neural networks. For the input saturation, the auxiliary dynamics is included to design the adaptive learning law. The neural weights and filtered tracking error are guaranteed to be bounded via Lyapunov approach. The effectiveness of the proposed method is verified by simulation of winged-cone model.

关 键 词：hypersonic flight vehicle no back-stepping neural network longitudinal dynamics STABILITY 

分 类 号：V212[航空宇航科学与技术—航空宇航推进理论与工程] V249.1