基于学习的高超声速飞行器分层协调容错方法  

Learning-based hierarchical coordination fault-tolerant method for hypersonic vehicles

作　　者：武天才 王宏伦[1,2] 任斌 严国乘 吴星雨 WU Tiancai;WANG Honglun;REN Bin;YAN Guocheng;WU Xingyu(School of Automation Science and Electrical Engineering,Beihang University,Beijing100191,China;The Science and Technology on Aircraft Control Laboratory,Beihang University,Beijing 100191,China;Hangzhou City University Binjiang Innovation Center,City College,Zhejiang University,Hangzhou 310056,China)

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100191 [2]北京航空航天大学飞行器控制一体化技术重点实验室,北京100191 [3]浙江大学城市学院滨江创新中心,杭州310056

出　　处：《航空学报》2024年第22期220-232,共13页Acta Aeronautica et Astronautica Sinica

基　　金：国家自然科学基金(62173022);航空科学基金(2018ZC51031);北京航空航天大学沈元学院卓越研究基金(230121205)。

摘　　要：为系统提升高超声速飞行器在不同严重程度的执行机构故障情况下的容错能力和任务完成度,提出一种基于深度学习的飞行器分层协调容错方法。首先,为实现执行机构故障严重程度的在线量化分析,提出了基于深度神经网络的飞行器可配平能力预示方法和可达区域边界预示方法;接着,以上述2种预示方法为“纽带”,构建分层协调容错的总体框架——根据预示结果在线判断当前执行机构故障的严重程度,并针对性地协调控制层、制导层和规划层的容错机制,来尽可能弥补执行机构故障对飞行性能和任务完成能力的影响;最后,在3种不同严重程度的故障情况下进行仿真来验证所提方法的有效性。To enhance fault-tolerance capability and mission completion ability of hypersonic vehicles under various severity levels of actuator faults,this paper proposes a learning-based hierarchical coordination fault-tolerant method for hypersonic vehicles.Firstly,to achieve online quantitative analysis of the severity of actuator faults,deep neural network-based approaches for predicting vehicle trim capability and reachable region boundaries are proposed.Subsequently,using the above prediction methods as“bridges”,a hierarchical coordinated fault-tolerance framework is constructed.The method assesses the severity of current actuator faults in real-time based on prediction results,then selectively coordinates fault-tolerant mechanisms at the control,guidance,and planning layers to mitigate the impact of actuator faults on flight performance and mission completion capability as much as possible.Finally,simulations under three different severity levels of actuator faults are conducted to validate the effectiveness of the proposed fault-tolerant method.

关 键 词：高超声速飞行器 执行机构故障 深度学习 可配平能力 可达区域 容错 

分 类 号：V249.1[航空宇航科学与技术—飞行器设计]